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Encountering Stronger Password Requirements: User Attitudes and Behaviors


Abstract and Figures

Text-based passwords are still the most commonly used authentication mechanism in information systems. We took advantage of a unique opportunity presented by a significant change in the Carnegie Mellon University (CMU) computing services password policy that required users to change their passwords. Through our survey of 470 CMU computer users, we collected data about behaviors and practices related to the use and creation of passwords. We also captured users' opinions about the new, stronger policy requirements. Our analysis shows that, although most of the users were annoyed by the need to create a complex password, they believe that they are now more secure. Furthermore, we perform an entropy analysis and discuss how our findings relate to NIST recommendations for creating a password policy. We also examine how users answer specific questions related to their passwords. Our results can be helpful in designing better password policies that consider not only technical aspects of specific policy rules, but also users' behavior in response to those rules.
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Encountering Stronger Password Requirements:
User Attitudes and Behaviors
Richard Shay
Saranga Komanduri
Patrick Gage Kelley
Pedro Giovanni Leon
Michelle L. Mazurek
Lujo Bauer
Nicolas Christin
Lorrie Faith Cranor
Carnegie Mellon University
Pittsburgh, PA
Text-based passwords are still the most commonly used au-
thentication mechanism in information systems. We took
advantage of a unique opportunity presented by a significant
change in the Carnegie Mellon University (CMU) computing
services password policy that required users to change their
passwords. Through our survey of 470 CMU computer users,
we collected data about behaviors and practices related to
the use and creation of passwords. We also captured users’
opinions about the new, stronger policy requirements. Our
analysis shows that, although most of the users were an-
noyed by the need to create a complex password, they be-
lieve that they are now more secure. Furthermore, we per-
form an entropy analysis and discuss how our findings relate
recommendations for creating a password policy.
We also examine how users answer specific questions related
to their passwords. Our results can be helpful in design-
ing better password policies that consider not only technical
aspects of specific policy rules, but also users’ behavior in
response to those rules.
Categories and Subject Descriptors
D.4.6 [Security and Protection]: Authentication; H.1.2
[User/Machine Systems]: Human factors
General Terms
Security, Human Factors
National Institute of Standards and Technology
Copyright is held by the author/owner. Permission to make digital or hard
copies of all or part of this work for personal or classroom use is granted
without fee.
Symposium on Usable Privacy and Security (SOUPS) 2010, July 14–16,
2010, Redmond, WA USA
Security, Usability, Passwords, Policy, Survey
One of the fundamental problems in computer security
is how to authenticate a user to a computer system con-
veniently and securely. Authentication is typically the first
step toward confirming that a user is authorized to perform a
requested action, be it retrieving email, withdrawing money
from an ATM, or issuing commands to a power-distribution
In recent years, a number of devices and techniques have
been proposed—including smart cards, RFID cards, USB
tokens, and graphical passwords—to make authentication
more usable, convenient, and secure. While each of these
technologies has its advantages and may be well suited for
use in a specific environment or for a specific application,
text-based passwords remain the most commonly used au-
thentication mechanism. This is in part because text-based
passwords require no special hardware and are easy for end
users to input and for system developers to implement.
Compared to more recent authentication technologies, such
as those that rely on public key cryptography, text-based
passwords are often seen as being less secure—easier to pre-
dict or guess for an adversary—thereby making it possible
for the adversary to impersonate a legitimate user and mis-
use his or her authority. In large part, this weakness of
text-based passwords is due to their brevity.
The issue of text-based passwords being drawn from a rel-
atively small space is compounded by the propensity of users
to select particularly weak passwords, such as those contain-
ing or based on dictionary words. This means that the space
of passwords actually used is much smaller than the space
of theoretically available passwords, dramatically increasing
the likelihood of an attacker being able to discover a given
password, through a brute-force attack or even guessing.
To combat both the inherent and user-induced weaknesses
of text-based passwords, administrators and organizations
typically institute a series of rules—a password policy—to
which users must adhere when choosing a password. A pass-
word policy may specify, for example, that a password must
have a minimum number of characters, that it must include
uppercase letters or numbers, and that it may not include
dictionary words. The purpose of such password policies is
to ensure that the space of possible passwords is large and
to prevent users from selecting passwords that may be easy
for an attacker to discover, whether by guessing or through
a brute-force attack.
There is consensus in the literature that a properly writ-
ten password policy can provide an organization with in-
creased security [10, 20, 21, 22, 23]. There is, however, less
accord in describing just what such a well-written policy
would be, or even how to determine whether a given pol-
icy is effective. Prior work has examined user habits with
passwords [7], simulated users [20, 21], performed labora-
tory experiments [14,23], studied lists of passwords [1], and
surveyed users with a static password policy [11, 16, 17, 24].
Despite this work, the effects of password policies are still
unclear, for two main reasons, detailed below.
First, although it is easy to calculate the theoretical pass-
word space that corresponds to a particular password pol-
icy, it is difficult to determine the practical password space.
Users may, for example, react to a policy rule requiring them
to include numbers in passwords by overwhelmingly picking
the same number, or by always placing the number in the
same location in their passwords. There is little published
empirical research that studies the strategies used by actual
users under various password policies.
Second, some password policies, while resulting in stronger
passwords, may make those passwords difficult to remember
or type. This may cause users to engage in a various be-
haviors that might compromise the security of passwords,
such as writing them down, reusing passwords across dif-
ferent accounts, or sharing passwords with others. Further
undesirable side effects of particular password policies may
include frequently forgotten passwords, which may increase
help-desk workload and thus IT-support costs [15]; discon-
tent among users forced to adhere to an inconvenient pass-
word policy; and generally diminished productivity [6, 7].
In fact, the harm caused by users following an onerously
restrictive password policy may be greater than the harm
prevented by that policy [6].
In this paper, we seek to advance understanding of the
factors that make creating and following password policies
difficult. We present the results of a survey of 470 Carnegie
Mellon University (CMU) students, faculty, and staff, con-
ducted soon after the university’s password policy under-
went an abrupt change from being very unconstrained, to
being very constrained. Because of this opportune timing,
we were able to gather data to compare user habits under
both policies. We also gathered users’ impressions shortly
after they experienced the policy shift and collected informa-
tion on password-selection strategies under the new policy.
Among our findings is that, unsurprisingly, users were an-
noyed by the shift to a stricter password policy. However,
we also found that users did not find it difficult to adhere to
this policy and believed that the change in policy resulted
in greater security. Users were neutral toward whether the
change was worth the effort, and whether CMU should re-
turn to its prior password policy. Relatively few users wrote
down their passwords, but a significant number reused their
passwords across accounts or shared them with others. In
analyzing these and other findings we also discovered statis-
tically significant differences between the behavior of differ-
ent demographic groups.
Our investigation included collecting statistics about some
very specific password-selection behaviors, such as which
digits users opted to use and the locations of these digits
in passwords. Roughly extrapolating from these results, we
estimate the practical entropy of passwords that adhere to
the new policy.
This paper is organized as follows. In Section 2, we discuss
the change in CMU’s password policy in more detail and
review related work. We describe the methodology of our
study in Section 3. In Section 4, we present our results on
user behavior: we report on demographics, user sentiment,
how users fared in creating and memorizing passwords, and
the sharing and reuse of passwords. In Section 5, we present
our findings about users’ password-construction strategies,
the entropy of passwords created under the new password
policy, and how this entropy compares with that predicted
by NIST. In Section 6, we highlight our most significant
In this section, we provide more detail about the Carnegie
Mellon password policy change and discuss related work.
2.1 CMU Password Policy Change
CMU students, faculty, and staff have accounts on the An-
drew computer system. These password-protected accounts
provide access to important online services, including email,
course registration, and access to licensed software. Until
the end of the Fall 2009 semester, the only password re-
quirement enforced for most Andrew users was that their
password include at least one character.
In December 2009, all Andrew users received an email an-
nouncing a new, stricter password policy. By January 27,
2010, all Andrew passwords would be required to contain
at least eight characters, and include at least one upper-
case letter, one lowercase letter, one digit, and one symbol.
Furthermore, new passwords would be subject to a dictio-
nary check: if the string obtained after removing all non-
alphabetic characters matched a dictionary word, the pass-
word would be rejected. Passwords containing four or more
occurrences of the same character would also be rejected.
When the new password policy went into effect on Jan-
uary 27, users without compliant passwords became unable
to access their Andrew accounts. By that evening, 88.2%
of the 14,587 student accounts were compliant, as well as
82.8% of the 4,511 staff accounts and 78.6% of the 2,005
faculty accounts. Some users may have failed to change
their passwords because they no longer used their Andrew
This change in password policy was motivated by the uni-
versity’s joining the InCommon Federation, “a formal feder-
ation of organizations focused on creating a common frame-
work for collaborative trust in support of research and edu-
The federation has entropy requirements for pass-
words for achieving specific identity-assurance levels: for ex-
ample, achieving bronze or silver identity-assurance status
requires the probability of successfully guessing a password
be no more than 1 in 1,024 or 1 in 16,384.
To create a silver password policy, CMU systems admin-
istrators used a government-issued password-entropy cal-
culation spreadsheet.
This spreadsheet, which associates
various password policy configurations with estimated per-
password entropy, is based on National Institute of Stan-
dards and Technology (NIST) guidelines for estimating how
many attempts are required to guess a password under a
given policy [2].
2.2 Related Work
Several studies have examined how password policies af-
fect users. In a study using password diaries, Inglesant et al.
identified tradeoffs between password strength and produc-
tivity at work. They found that although users were aware of
the security concerns that influence password requirements,
users rarely changed their passwords, and password policies
did not account for sensitivity variations in the resources
they protect [7]. This work, like ours, used feedback from
real users to examine the effects of password policy. Un-
like our survey, however, this study focused on gathering
detailed information from a small set of users.
Leyden reported on an informal survey in which 90% of
152 computer system users divulged their passwords in ex-
change for a pen. The survey also found that users tended
to use simple passwords, with “password” itself used 12% of
the time [11]. A SafeNet survey asking thousands of users
about their security habits and their organizations’ secu-
rity policies found that about half of the respondents wrote
down their passwords and about 80% had three or more
passwords [16].
Surveying 123 computer science undergraduates on pass-
word sentiment, Hart et al. found most were not concerned
about password security. Around 20% of students shared
their passwords and about 30% used the same password
four or more times [5]. Our survey, unlike that of Hart et
al., included faculty and staff as well as students, and was
administered to a larger set of users.
In a 1999 survey of computer users at a Department of
Defense installation in California, Zviran and Haga collected
user demographics, computer usage information, and pass-
word characteristics from 860 people, and then explored
associations within the data. They found that the most
common password length was six characters, 80% of users
had entirely alphabetic passwords, 80% never changed their
passwords, and 35% never wrote their passwords down. They
found no association between password composition and the
sensitivity and importance of the data being protected or
between the method of selecting a password and the impor-
tance of the data [24]. In the Zviran and Haga study, unlike
ours, users did not seem to be required to observe a strict
password policy.
Shay et al. used computer simulation to analyze the re-
sults of various password policies on overall organizational
health. The results indicated that although a password pol-
icy that allows weak passwords can lead to system compro-
mise, an overly strong policy can lead to users writing down
passwords and thereby increase system vulnerability [20,21].
Other prior work has examined password policies using
laboratory studies. Proctor et al. and Vu et al. found that
passwords made under stronger restrictions can be harder
to crack using automated tools but also harder to create
and remember [14]. Vu et al. also found that that the
likelihood of remembering a specific password diminished as
more passwords were remembered [23]. Bishop et al. used
automated tools to investigate the difficulty of cracking real
password files provided by systems administrators [1].
Kuo et al. found that automated cracking tools were less
successful against mnemonic passwords than against control
passwords. Also, the authors demonstrated the feasibility of
building mnemonic password dictionaries based on popular
phrases [10]. Kuo et al. examined passwords created specif-
ically for the study, as compared to our survey of passwords
in regular use by users.
Using an opt-in component of the Windows Live Tool-
bar, Florencio et al. captured password information for more
than half a million people. On average, each person used
seven passwords across 25 different websites. The authors
found that the strongest passwords were used on just a few
websites, while weaker passwords were reused more often [3].
Florencio et al. examined passwords across a wide variety
of account types and unspecified password-strength require-
ments; our study, by contrast, focuses on a specific password
policy. Our study also considers user sentiment toward pass-
word policy in addition to password strength and reuse.
Our work also differs from each of the mentioned studies
in that we considered users of one specific system just as
a less constrained password policy was replaced by a more
constrained one. As a result, we gathered data about a single
set of users in one system under two very different password
In February 2010, we conducted a paper-based survey of
470 CMU students, faculty, and staff who had changed their
Andrew password to comply with the new requirements. We
designed our survey to collect data on password handling,
password composition, password storage and reuse, and user
sentiment about the new password requirements. In this
section, we discuss our survey design, our survey questions,
and our survey administration and analysis process.
3.1 Survey Design
We set out to design a survey that would elicit truthful in-
formation about passwords from a cross-section of university
students, faculty, and staff. While collecting and managing
the data would have been easier online, we were concerned
that more security-savvy users would be reluctant to provide
truthful information if they thought we could link their re-
sponses to their usernames. The CMU Information Security
Office (ISO) also expressed concern that an online survey
asking users to divulge information about their passwords
would conflict with advice in their cyber-security awareness
campaigns and confuse users. Therefore, we opted to con-
duct the survey on paper, asking passersby on the CMU
campus to complete survey forms.
We designed the survey form to fit on both sides of a single
sheet of paper and expected that typical participants would
take about four minutes to complete it. In order to keep the
survey short, we limited the number of questions we asked in
each category and did not include any open-ended questions.
While pilot testing the survey, we received feedback that
our password composition questions made respondents un-
comfortable. Pilot testers expressed concern that we were
gathering so much specific data about their passwords that
we might be able to determine them. We feared that these
concerns would prevent users from taking our survey or
cause them to answer untruthfully. To reduce the amount
of information we might collect about any given user’s pass-
word, we created two variants of the survey form, A and
B, and divided the password-composition questions between
them. In addition, for questions that users might have been
uncomfortable answering, we included an “I prefer not to
answer” option.
3.2 Survey Questions
We give an overview of our survey questions here. We pro-
vide the front of the survey form and the A and B variants
of the back in Appendix A.
Demographics We asked four demographic questions: role
at CMU, gender, age, and whether the respondent was ma-
joring or had a degree or job in the information technology
(IT) field.
Password Handling Next, we asked eight questions about
when and how respondents created their password and how
they have handled it since. Respondents were first asked
when they changed their passwords to comply with the new
policy. The response options included “I have not changed
my password to comply with the new policy yet.” Surveys
with this response indicated were dropped from the results,
since the target population was users who had complied with
the new policy. Respondents were asked, “If you tried to lo-
gin to your Andrew account right now, how many attempts
do you think it would take?” to gauge their confidence in re-
calling their password. Respondents were also asked whether
and with how many people they shared their old and new
passwords, and whether they had forgotten their new pass-
word and, if so, how they recovered it. We asked two ques-
tions about strategies used in creating their new password.
Finally, all respondents were asked how many tries it took
to create an acceptable password.
Password Composition All respondents were asked the
length of their passwords and which symbols they used. Sur-
vey A asked how many uppercase and lowercase letters were
used, and in which positions numbers and symbols occurred.
Survey B asked how many symbols and numbers were used
and in which positions uppercase letters occurred. Each of
these questions included the response option of “I prefer not
to answer.”
Password Storage and Reuse Respondents were asked
whether they have written down their current password, ei-
ther on paper or electronically. If so, they were asked to
indicate how it is protected, selecting from eight response
choices including, “I do not protect it” and “Other” followed
by a blank line. The question was then repeated for their old
password. Respondents were then asked if they have a set
of passwords they reuse and whether they have a password
for different accounts reused with slight modification.
User Sentiment Finally, respondents are presented with
six Likert questions concerning their sentiment regarding the
policy change. Five of the questions were constant between
surveys. Survey A asks users “Creating a password that
meets the new requirements was difficult” while survey B
replaces the word “difficult” with “easy.” Other questions
included “With the new password requirements, my Andrew
account is more secure”, “Creating a password that meets the
new requirements was annoying”, “Creating a password that
meets the new requirements was fun”, “Any added protection
provided by the new password is worth the added effort of
creating/remembering/using it”, and “I would like Andrew
to go back to the old password policy”. Response options for
all six questions were “Strongly Agree”, “Agree”, “Neutral,
“Disagree”, and “Strongly Disagree”.
3.3 Survey Administration and Analysis
We distributed surveys within a two-week period in Febru-
ary 2010, soon after the January 27 password-change dead-
line. Researchers went to areas on the CMU campus where
students, faculty, or staff were likely to congregate, including
the student center, dining areas, library, classroom build-
ings, and department offices. Potential respondents were
approached by researchers and asked if they would like to
take a survey. If asked, researchers told respondents that the
survey was about the recent password policy change. Re-
spondents were offered a selection of candy bars and snacks
for their participation. Having the same group of researchers
handing out surveys, all within a short period of time, helped
prevent participants from taking the survey more than once.
We entered the responses to each survey form into an
online survey management system. Before conducting a
detailed analysis of the results, we disqualified surveys in
which the respondent indicated that he or she had not yet
changed his or her password. We also disqualified surveys
in which the respondent answered the password composition
questions in a way that was inconsistent with the new re-
quirements (for example, some respondents said their new
password was fewer than eight characters or did not contain
a symbol or a number). Some participants opted against
answering all questions on the survey, either by leaving a
response choice blank or by selecting the “I prefer not to an-
swer” response choice when available. We did not disqualify
any surveys for blank responses.
We collected 492 surveys and disqualified 22 of them based
on the criteria described in Section 3.3. This section presents
results about user behavior based on our analysis of the
remaining 470 surveys. We first detail the demographics
of our participants, followed by our findings related to user
sentiment. We next describe how users transitioned to the
new password policy, coping strategies they employed, and
their tendency to share their passwords. Finally, we examine
the rate of user refusal to answer our password composition
4.1 User Demographics
We categorized participants according to gender, age, role
on campus, and IT experience. Since a large number of our
participants were undergraduate students, we focused our
analysis on two age groups: those under 22 (mostly under-
graduates), and those 22 and older (graduate students, fac-
ulty, and staff). As illustrated in Figure 1, the mean age
for students (21.0) is significantly different from the faculty
(38.2), staff (50.4), and “other” (29.3) groups (p < 0.0001,
Kruskal-Wallis test). IT experience was evaluated by ask-
ing, “Are you majoring in or do you have a degree or job in
computer science, computer engineering, information tech-
Number Percent
Male 228 48.5%
Female 241 51.3%
No Answer 1 0.2%
<22 280 59.6%
22 185 39.4%
No Answer 5 1.1%
Faculty 10 2.1%
Staff 41 8.7%
Student 413 87.9%
Other 6 1.3%
IT Major/Job
Yes 162 34.5%
No 301 64.0%
No Answer 7 1.5%
Table 1: Participant demographics
Figure 1: A comparison of university role and age.
nology, or a related field?”. Table 1 and Figure 1 summarize
the basic demographics of our participants.
These four demographic groups are not independent. For
example, while 73% percent of IT participants are male,
only 35% of non-IT participants are; this difference is sig-
nificant (p < 0.0001, Fisher’s exact test, or FET). Addi-
tionally, when looking at the relationship between students
and other participants, both the male/female dichotomy and
the percentage of IT experienced participants are significant
(p < 0.0001, FET). These relationships between our demo-
graphic groups are shown visually in Figure 2.
Our survey sample is not entirely representative of the
CMU population or the population of Andrew account hold-
ers, which is 70.6% student, 8.6% faculty, 20.5% staff, and
0.2% other. In our sample, faculty and staff are somewhat
underrepresented. In addition, while the CMU student pop-
ulation is 64% male,
the students in our sample were only
52% male. Our ratio of males to females differs significantly
from the CMU population (χ
test, p value < 0.0001).
4.2 User Sentiment
Survey respondents reported their opinions on the pass-
word policy change by responding to six 5-point Likert ques-
Figure 2: Significant relationships among demo-
graphic constituents. All relevant p values < 0.0001.
Mode Mean Std. Dev.
Secure 4 (agree) 3.6 0.92
Easy 4 (agree) 3.2 1.09
Annoying 4 (agree) 3.7 1.05
Fun 2 (disagree) 2.2 0.97
Worth the effort 3 (neutral) 3.4 0.96
Back to old system 3 (neutral) 2.8 0.91
Table 2: Participant responses on a Likert scale (1
being “strongly disagree,” 5 being “strongly agree”).
For “Easy,” approximately half of the participants
were assigned each phrasing of the question; for this
table, results for the “difficult” phrasing were re-
versed and combined with “easy.”
tions. The responses are summarized in Table 2. Overall,
respondents did not have strong feelings about the policy
Our first question attempted to gauge perceptions of the
overall value of the password change, with respondents agree-
ing somewhat that their accounts are more secure as a re-
sult of this change. Respondents also indicated that they
found the process of creating a new password fairly easy.
On the other hand, respondents also found the password
change somewhat annoying and disagreed that creating a
new password was fun.
The overall acceptance that the new requirements improve
security combined with the sentiment that changing pass-
words was annoying helps to explain the overall neutral re-
sponses about whether it was worth the effort to switch and
whether the Andrew system should switch back to the old
Opinions on whether the password change was easy or
difficult were consistent across the two survey versions; par-
ticipants agreed that the change was easy and disagreed with
it being difficult. A Wilcoxon test with continuity correc-
tion and µ = 3 indicates that the questions appear to be
symmetric (p = 0.25).
We found only one significant difference in user sentiment
results among demographic subgroups: IT participants were
significantly more opposed to the idea of returning to the old
policy (mean 2.6) compared to non-IT participants (mean
1 2 3 4 5 6
To create 59.6% 21.3% 11.1% 2.8% 3.7% 1.5%
To login 79.4% 17.1% 3.3% 0% 0.2% 0%
Table 3: Number of attempts respondents reported
needing to create a new password and number of
attempts they expect it would take to log in using
that new password.
Forgot Had not forgotten
Total Participants 87 375
chi-squared p-value
Tries to create 2.3 1.7 13.97 0.0019
Tries to log in 1.5 1.2 24.11 <0.0001
W p-value
Annoying 4.2 3.6 1.1·10
Fun 1.9 2.2 1.9·10
Table 4: Significant participant comparisons be-
tween users who have forgotten their passwords and
those who have not. Kruskal-Wallis tests were used
to compare “Tries” and Wilcoxon tests were used for
Likert values. Holm-corrected p values are shown.
2.9). This difference, calculated using a Wilcoxon test with
continuity correction, yields a Holm-Bonferroni-adjusted p
value of 0.0048.
4.3 Transitioning to the New Policy
We examined how users transitioned to the new password
policy, focusing on how they fared in creating and memoriz-
ing their new passwords.
Twenty percent of participants changed their password
the same day they received the first request email; another
32% did so before receiving a second email. Forty-six percent
changed after receiving multiple emails.
Participants reported that it took on average 1.77 tries
to create a password the system accepted. Participants also
expect it would take 1.25 tries to log in with their new pass-
words. These responses are detailed in Table 3.
Our results depict a portion of users appearing to struggle
with the policy change; this is consistent with previous re-
search that has shown more complex passwords to be more
difficult to recall [9]. Eighty-seven participants, or 19%, re-
ported forgetting their new password. These users show a
number of significant differences from users overall, as shown
in Table 4. They reported needing significantly more at-
tempts to create and log in with a password under the new
policy. They also found creating a new password more an-
noying and less fun.
Among users who had forgotten their current password,
60% recovered it by remembering it later, 21% retrieved
it from where it was written, and 11% went to the help
desk to have it reset. As might be expected, those who had
forgotten their password were also four times more likely to
have written their current password down.
Using a logistic regression model, we determined that the
likelihood of forgetting the current password was related to
several demographic factors. Faculty and staff were three
Current Old
Password Password
I do not protect it 31% 18%
I hid it 16% 20%
I stored it on a computer or device
protected with another password 13% 24%
I stored it in an encrypted file 11% 11%
I locked up the paper 9% 4%
I wrote down a reminder instead
of the actual password 7% 16%
Table 5: How users protected their written-down
times more likely to have forgotten their passwords than
students (β = 1.134, p = 0.025). Women were almost twice
as likely to forget as men (β = 0.627, p = 0.029), and those
who changed their password early were less likely to forget
it (β = 0.566, df = 2, p = 0.002). Forgetting was not signif-
icantly related to IT experience or age.
4.4 Coping Strategies
Several survey questions concerned how users are coping
with the policy change. These questions asked about pass-
word reuse between accounts and writing passwords down.
More than 80% of participants (381) reported reusing a
set of passwords in different places. A total of 281 users said
they use one password with slight modification for different
accounts. Most of the users who reuse a set of passwords
also use one password with slight modification on different
accounts (254 of the 381). Women were more likely than
men to reuse a password with slight modification: 69% of
women compared to 55% of men (p = 0.021, FET).
Only 63 participants (13%) reported writing down their
current password, 40 on paper and 23 electronically. Fifty-
six (12%) had written down the old password. Only 27 peo-
ple wrote down both passwords; almost half of those are
faculty or staff. Table 5 details how users who wrote down
their passwords protected them.
Table 5 shows that although similar numbers of users
wrote down old and new passwords, users took more precau-
tions to secure their old passwords. This may result from
newer passwords not being as well memorized due to their
novelty or their complexity; users may keep them in a less
secure location because they consult them more frequently.
A survey conducted in 2004 found that approximately half
of surveyed employees wrote down their passwords [16]. An-
other survey conducted by Zviran et al. reported 65% of
employees writing down their passwords [24]. These figures
are larger than the 13% of users who did so in our survey.
While many previous papers have discussed the phenomenon
of users writing down their passwords [1,4,21,23,24], we ob-
served users writing down their password to be less common
than users reusing passwords.
4.5 Password Sharing
One hundred and twenty-seven participants (28%) said
they had given their old or current password to at least one
This result is also supported by a logistic regression analysis
including gender, age, role on campus, and IT experience.
Of these, only the coefficient of gender was significant (p
0.001) with all other coefficients not significant (p > 0.1).
Figure 3: A comparison of sharing for old and new
other person. Figure 3 illustrates how many people these
passwords were shared with.
Fifty-two participants shared both their old and current
password, sixty-seven participants shared only their old pass-
word, and six shared only their current password. This
discrepancy may indicate that, since the current passwords
are relatively new, occasions to share them have been less
frequent. Overall, IT participants were less likely to share
passwords with other people (p < 0.0001, Holm-corrected
FET). Thirty-three percent of participants younger than 22
(our “undergraduate” group) shared their passwords, com-
pared with 20% for the rest of the participants. This indi-
cates that undergraduates are more likely to share (p = .027,
Holm-corrected FET).
4.6 Refusal to Answer
One potential sampling bias for a voluntary survey such
as this is that there may be a tendency for less security-
minded users to participate. Furthermore, among survey
respondents, security-minded users may be more likely to
refuse to answer security-sensitive questions. A number of
questions in the survey asked about the structure of the
password (e.g., the position of the required symbols in the
password, or the nature of these symbols). All of these ques-
tions had an “I prefer not to answer” choice, leaving the door
open for respondents to refuse to answer. We also assume
that a respondent who left a question blank was refusing to
We next turn to an analysis of the patterns we
observed in the refusals to answer.
In this analysis, participants are separated into full dis-
closure and minimal disclosure groups. This corresponds to
answering all or none, respectively, of the subset of ques-
tions dealing with password structure.
One hundred and
The special-character position questions (Q17A, Q18A,
and Q17B) are an exception. For these questions, respon-
dents only counted as refusing to answer if they explicitly
selected the “I prefer not to answer” choice. This is because
we suspect respondents were confused by the available an-
swer choices.
Specifically, questions 13 through 18 on survey A and 13
through 17 on survey B were considered. Question 18 on
survey B was not considered because most participants an-
swered this question, even if they declined to answer the
previous five questions.
Question Summary Percent Answering
Q13. Password Length? 73%
Q14. What symbols in password? 46%
Q15A. How many lowercase letters? 65%
Q16A. How many uppercase letters? 68%
Q17A. In which positions are numbers? 65%
Q18A. In which positions are symbols? 66%
Q15B. How many symbols? 62%
Q16B. How many numbers? 66%
Q17B. In which positions are uppercase letters? 66%
Q18B. Contains number related to year created? 88%
Table 6: Proportion of participants who answered
each question (out of the number of participants who
took each survey version). Question summaries are
given; their full text is found in Appendix A.
nine participants answered all these questions (full disclosure
group), while 69 did not answer any of them (minimal dis-
closure group). The rest of the participants (292) answered
some, but not all of the questions.
We found that participants in the full disclosure group
were more likely to reuse their passwords: 92% of them ac-
knowledged that they do, significantly more than the 81% of
people reusing passwords outside of the full disclosure group
(p = 0.011, FET). Conversely, in the minimal disclosure
group, only 66% of participants indicated they reused their
passwords, significantly less (p = 0.0002, FET) than the
86% of participants outside the minimal disclosure group.
In short, the more defensive a user is about sharing innocu-
ous, but specific, password information in a survey, the less
likely he or she is to engage in the practice of reusing pass-
A secondary finding was that IT people were less likely
to disclose the structure of their passwords. The full dis-
closure group contained only 23% IT people, while the rest
of the sample contained 39% IT people (p = 0.004, Holm-
corrected FET). Likewise, IT people make up 54% of the
minimal disclosure group, while the rest of the sample con-
tains the significantly lower proportion of 32% (p = 0.031,
Holm-corrected FET).
The proportion of participants who answered each of these
questions is shown in Table 6. Pairwise Wilcoxon tests
were performed to group questions with a statistically sim-
ilar number of responses. From this analysis, three groups
emerge: question 14 with 46%, 18B with 88%, and the re-
maining questions with an approximately 66% answer rate.
The mean number of responses differ among these groups
with Holm-corrected p < 0.005. The differences among
means for the remaining questions are not significant after
Users were especially unlikely to answer Q14, which asked
for the specific symbols in a participant’s password. No
other question asked for specific characters. Q18B was sig-
nificantly more likely to be answered than the other ques-
tions. This may be because it is a question which can be
answered with a simple “No.”
This section presents our findings regarding password cre-
ation strategies, password configuration, and password en-
tropy. It then discusses how our entropy calculations com-
pare with those published by NIST.
Question Summary Number Percent
Modified old password 243 52.4%
Created entirely new password 138 29.7%
Modified password from elsewhere 52 11.2%
Reused password from elsewhere 17 3.7%
Reused old Andrew password 11 2.4%
Modified a currently-unused password 3 0.6%
Table 7: Responses to Question 11: “When you cre-
ated your current password, which of the following
did you do?”. Derived from “Other” answers.
Question Summary Number Percent
Word/name w. numbers/symbols
added to beginning/end 204 43.4%
Based on a name 163 34.9%
Word/name w. numbers/symbols
replacing some letters 79 16.8%
Based on non-English word 67 14.3%
Based on a birthday 43 9.1%
First letter of words in phrase 26 5.5%
Word/name with missing letters 20 4.3%
Based on address 11 2.3%
Based on phone number 10 2.1%
Table 8: Responses to Question 12: “Did you use
any of the following strategies to create your current
password (choose all that apply)?” More than one
answer could be selected.
5.1 Password Creation Strategies
We asked participants if they had created a new password
to meet the new requirements or modified an old one. As
shown in Table 7, less than 30% of respondents created an
entirely new password.
Strategies used by participants in constructing their pass-
word are shown in Table 8. Many participants used multiple
strategies. For example, one participant used four strategies:
based on a non-English word, numbers or symbols added to
the beginning or the end, numbers or symbols substituting
for letters, and missing letters. Thirty-six percent of users
who reported employing any of these strategies reported us-
ing more than one.
Combining the numbers in Table 8: 210 users based their
password on a word,
163 based their password on a name,
56 used public information (an address, phone number, or
birthday), and only 26 use a mnemonic-style password (where
a password is constructed from the first letters of a phrase).
Use of these strategies was not significantly related to any
demographic factors, and there were no strong correlations
between strategies.
5.2 Password Configuration
Table 9 summarizes length and character class information
reported in our sample, and Figure 4 shows the distribution
of password lengths. The mean password length was 10.5
characters, with a mode of 8.
Since the new password policy requires users to change
their passwords annually, we thought some users might in-
clude a number related to the year the password was cre-
This number combines participants who selected one of the
“Based on a word” options and subtracts those participants
who also selected “Based on a name.”
Question Summary Mean σ
Password length 10.49 2.86
Number of lowercase letters 5.94 3.14
Number of uppercase letters 1.54 1.15
Number of numbers 2.70 1.88
Number of symbols 1.39 0.90
Table 9: Length and character class numbers for
passwords created under the new policy.
Symbol Freq. Percent Symbol Freq. Percent
! 86 39.6% ~ 2 0.9%
@ 27 12.4% % 2 0.9%
# 20 9.2% ( 2 0.9%
* 16 7.4% ; 2 0.9%
. 13 6.0% ^ 1 0.5%
$ 11 5.1% 1 0.5%
& 10 4.6% [ 1 0.5%
- 10 4.6% ] 1 0.5%
? 8 3.7% 1 0.5%
< 7 3.2% > 1 0.5%
: 6 2.8% _ 0 0%
, 5 2.3% { 0 0%
/ 5 2.3% } 0 0%
) 4 1.8% | 0 0%
+ 4 1.8% \ 0 0%
= 3 1.4% " 0 0%
Table 10: Frequency of occurrence for all 32 non-
alphanumeric characters that count as symbols in
the new password policy. Data is from the 217 users
who answered this question.
ated. However, only seven of 208 users who answered Ques-
tion 18B indicated that their password contained a number
related to the year it was created. Unfortunately, we do
not know how many users were aware of the annual pass-
word change requirement, because we did not want to prime
participants with potentially negative information about the
new policy.
Table 10 summarizes the use of symbols in participants’
passwords. This table displays data for all 32 characters that
are counted as symbols, according to the policy provided by
the campus Information Security Office. It is notable that
the three most popular choices correspond to the symbols
produced by pressing Shift and the numbers 1, 2, or 3 on
a standard keyboard.
Many of our results in this section are presented using data
from the full disclosure group described in Subsection 4.6.
These 109 participants provided us with a nearly complete
picture of their passwords. Their passwords average 10.1
characters, which is significantly shorter than the 10.7 char-
acters of the rest of our sample. In all other ways, their
password composition data is not significantly different from
those of the other participants.
Character positions for the full disclosure group are shown
in Table 11. Our survey also included an “Other” answer
choice intended to determine the percentage of users with
special characters in the middle of their password. Unfortu-
nately, among all 470 responses, not a single one indicated
“Other” for any of the character position questions. We
have anecdotal evidence that participants may have misin-
terpreted the “Other” answer choice. Thus, we assume that
participants correctly indicate the special characters in the
Figure 4: The lengths of passwords under the new policy.
Character First Second from last Last
Numbers 11.6% 7.0% 48.8% 34.9%
Symbols 2.3% 11.6% 32.6% 55.8%
Uppercase Letters 74.2% 15.2% 4.5% 10.6%
Table 11: Position percentages for the 109 partici-
pants who answered all of the questions about their
password configuration. Percents may sum to above
100% due to users using multiples of the same char-
acter type.
Figure 5: This figure displays percentages for the
109 participants who answered all of the questions
about their password configuration.
first and last two positions of their password, but we make
no inferences about the middle of the password.
Figure 5 shows a composite picture of the full disclosure
group’s first and last two characters, produced by normaliz-
ing the positional percentages between survey versions and
inferring lowercase characters into the remaining spots. We
used this as part of our entropy estimate for the full disclo-
sure group’s passwords.
5.3 Entropy Calculation
Entropy can be described as a measure of how hard it is
to predict the value of a variable. More specifically, entropy
can be considered a measure of the difficulty of guessing a
password [2]. In general, the more entropy there is within
a given distribution of passwords, the more difficult it is to
Entropy in Length 2.68
Entropy in Numbers
How many numbers 2.31
Where they are 1.66
What they are log
(10) = 3.32
Total 7.29
Entropy in Symbols
How many symbols 0.90
Where they are 1.48
What they are 3.56
Total 5.94
Entropy in Uppercase
How many uppercase 1.15
Where they are 1.29
What they are 1.42 · 2 = 2.84
Total 5.28
Entropy in Lowercase
How many lowercase 0.00
Where they are 0.00
What they are 4.91 · 2 = 9.82
Total 9.82
Total Entropy 31.01
Table 12: Password entropy estimates, in bits, of
each facet of a password. Entries that are 0 have
no entropy because they are known deterministi-
cally once the other facets of a password are known.
These estimates were derived from the responses
of the 109 participants in the full disclosure group.
See text for an explanation.
guess a password that was selected from that distribution.
The entropy of a password distribution is important because
it lower-bounds the expected number of guesses required
by an attacker [12]. Passwords with larger entropy values
require a larger expected number of guesses, making entropy
useful as a measure of password strength.
The formula used in this paper to estimate entropy was
established by Claude Shannon [19]. Briefly, Shannon’s for-
mula transforms a distribution into an entropy estimate.
Using an optimal strategy, E[G] 2
+ 1, where E[G]
is the expected number of guesses and H is the entropy in
bits [12].
For each possible value x of a variable, the probability of
that value occurring is p(x). The entropy is then calculated
as H =
[p(x) lg p(x)]. It is typically measured in bits.
Because Shannon’s formula for entropy is additive, we are
able to calculate entropy for a distribution of passwords as
a whole by summing the entropy derived from individual
facets of those passwords. We can separately estimate the
entropy derived from password length, character placement,
number of each character type in the password, and the con-
tent of each character; and then combine these to form an
estimate of the total password entropy.
An example will clarify the calculation; let us consider
the entropy derived from the length of a password. The
probability of a password having a specific length is deter-
mined by dividing the number of passwords that have that
length by the total number of passwords in a distribution.
Since 28% of respondents in the full disclosure group had
passwords of length 8, the probability of this length is 0.28.
Shannon’s formula is then applied to the probabilities for
all lengths, which we calculate from our survey data from
the full disclosure group. This calculation gives an entropy
of 2.68 bits for password length, indicating that password
length contributes this amount to the total entropy of pass-
words under our password policy.
The entropy contributed by many facets of passwords,
such as character placement and number of each character,
can similarly be computed using our survey data. We also
collected data on which symbols were used by specifically
asking participants to list the symbols in their passwords.
However, we did not collect similar data for numbers or let-
ters in passwords. Those facets for which entropy could not
be computed are denoted with a in Table 12. One of them
is “what the numbers are,” which we estimate to be equal
to the entropy of a single random digit, although partici-
pants actually used an average of 2.45 digits per password.
A similar issue occurs with lowercase and uppercase letters.
Since we do not have exact data on what these are, we use
the NIST estimate of 2 bits per letter for their entropy [2].
The numbers 1.42 and 4.91 in Table 12 represent the aver-
age number of uppercase and lowercase letters, respectively,
found in the full disclosure group’s passwords. Finally, en-
tries in Table 12 that are 0 have no entropy because they are
known deterministically once the other facets of a password
are known.
In this way, we can calculate an estimate of the entropy
of the different facets of the CMU password space. By sum-
ming these values, we derive an entropy estimate of the pass-
words as a whole. The components of this summation are
shown in Table 12. Our estimated entropy for CMU pass-
words is about 31 bits.
This estimate may be an overestimate because it does not
account for correlations between facets. To illustrate, con-
sider the placement of symbols in a password. If certain
symbols are only found in certain positions, the probabili-
ties associated with symbol placement will be dependent on
symbol content. This decreases the total entropy.
we do not have data at this level, we are forced to ignore such
correlations. On the other hand, Shannon’s entropy formula
produces an underestimate of the true entropy when applied
to a small random sample of a population [13]. Further, our
entropy calculation does not consider the fact that the CMU
password policy institutes a dictionary check, because we do
not have data on how this check affected entropy. This is
explained in more detail in Section 5.5.3.
As given by Shannon, the joint entropy H(X, Y ) H(X)+
H(Y ) where H(·) is the entropy function [19].
Question Summary Estimated Entropy in Bits
Q14. What symbols in password? 4.95
Q15A. How many lowercase letters? 3.43
Q13. Password Length? 2.97
Q16B. How many numbers? 2.52
Q17A. In which positions are numbers? 1.99
Q15B. How many symbols? 1.87
Q17B. In which positions are uppercase letters? 1.61
Q16A. How many uppercase letters? 1.38
Q15B. How many symbols? 1.11
Q18B. Contains number related to year created? 0.21
Table 13: The estimated entropy per question. This
table shows the amount of information revealed by a
participant answering each of these questions. This
is a cumulative score based on 3.56 average bits per
symbol and 1.39 average number of symbols revealed
(3.56 · 1.39 = 4.95). The proportion of participants
answering each question is found in Table 6.
5.4 Entropy and Refusal to Answer
Our results indicate that participants were less likely to
answer questions that divulge more about their passwords.
Table 13 shows the amount of information derived from the
answers to each survey question, as determined by calculat-
ing Shannon entropy over its given responses. The results
given in Table 13 differ slightly from those in Table 12 be-
cause they are over all responses, not just those from the
full disclosure group.
Participants’ refusal to answer a question, as shown in
Table 6, was strongly correlated with the amount of infor-
mation disclosed by that question (r = 0.74). This suggests
that participants intuitively know which questions uncover
more information about their passwords. In particular, as
mentioned in Section 4.6, participants were especially un-
likely to answer Q14 (what symbols), which reveals the most
information about their passwords; and especially likely to
answer Q18B (contains year), which reveals the least infor-
mation about their passwords.
5.5 Comparison to NIST Estimate
As mentioned in Section 2.1, the change to the CMU pass-
word policy was based on NIST guidelines that provide a
heuristic for estimating the entropy of user passwords [2].
In this section, we present some of the NIST assumptions
and compare them with our own results.
5.5.1 Password Length
The NIST heuristic assumes passwords will be exactly the
minimum length. However, as shown in Figure 4, only 24%
of respondents (83/343) reported a password of length 8. If
an attacker assumes a password is of minimum length, he or
she only has a 24% chance of successfully cracking a user’s
password after exhausting the entire 8-character password
space. We estimate that length contributes 2.68 bits of en-
tropy to passwords under the CMU password policy.
5.5.2 Special Characters
The NIST guidelines make the following assertion about
special characters, which include uppercase letters, numbers,
and symbols:
...the assumption here is that users will choose
passwords that are almost entirely lower case let-
ters, unless forced to do otherwise, and [rules]
that force them to include capital letters or non-
alphabetic characters will generally be satisfied
in the simplest and most predictable manner, of-
ten by putting a capital letter at the start ... and
punctuation or special characters at the end...
The NIST heuristic awards a flat 6 bits of entropy for the
inclusion of uppercase and non-alphabetic characters and 2
bits of entropy for each character, regardless of character
type. Applying this formula to the CMU password policy
yields an entropy contribution of 12 bits from the special
characters, if we assume a password of eight characters in-
cluding three special characters: one uppercase letter, one
number and one symbol (6 + 3 2 = 12).
In contrast, our results find that much of the entropy con-
tributed by uppercase and non-alphabetic characters comes
from users exceeding the minimum requirements: using more
numbers than necessary and varying the positions of spe-
cial characters. Our participants frequently chose passwords
that exceeded the minimum requirements of the policy. Only
31% (49/156) reported having a single number; 73% (115/158)
and 76% (111/146) reported having only one uppercase let-
ter or symbol, respectively. In addition, many respondents
reported having uppercase letters at the end of their pass-
words, or numbers or symbols at the beginning.
We es-
timate the cumulative entropy of these character types per
password to be 18.51 bits. This is found by summing the
entropies contributed by special characters in Table 12.
5.5.3 Dictionary Checks
Forty-five percent of our participants based their password
on a word, despite the fact that the CMU password policy
includes a dictionary check. This dictionary check operates
by removing all non-alphabetic characters from a password
and checking the remaining string against a dictionary. Thus
even if a password does contain a dictionary word, as long
as there are additional letters before, after, or within the
word, it satisfies the requirement. Concatenation of multiple
words is also allowed. This checking algorithm is consistent
with NIST’s conception of a dictionary check, which in the
NIST heuristic contributes a bonus of 6 bits to a password’s
Although we expect this check may increase the entropy
of passwords as a whole, we ignore the effect of this check in
our estimate. Validating it requires complete knowledge of
a user’s password, and our survey does not have this level of
detail. What we do know is that, despite this check, almost
half of our participants claimed to have a password based
on a word.
5.5.4 Defining Requirements
Although our calculations and the heuristics of NIST ap-
proach the problem of defining entropy for a given password
policy quite differently, both depict the CMU password pol-
icy as having a per-password entropy of roughly 30 bits.
However, there are other password policies which could
generate this level of entropy, beyond just the policy re-
quirements set forth at CMU. Using the NIST heuristic, a
randomly-assigned 5-character password has an entropy of
27% and 18% of respondents had numbers or symbols in
the first two characters of their passwords, respectively; and
22% had an uppercase letter in one of the last two positions.
33 bits. A 14-character user-chosen password with no spe-
cial character requirements would also have about 30 bits
of entropy, using the NIST heuristic. Though previous work
has found that long, passphrase-style passwords lead to more
typographic mistakes and user dissatisfaction [8], the results
of our survey suggest that some users prefer them. As seen
in Figure 4, 11% of our respondents currently have pass-
words that are 14 characters or longer. These users do not
need the additional restrictions of a strict password policy
to have a sufficiently strong password. Other users might
favor randomly-assigned, shorter passwords. If all of these
policies have sufficient entropy, it may be acceptable to allow
users to choose the policy that fits them best. This would
maintain security while improving usability.
This work presents several new insights regarding user
attitude and behavior under strict password policies. The
highlights are presented below.
Users find new requirements annoying but believe
they provide security. Our survey was conducted on a
population shortly after a required password policy transi-
tion from a less-constrained policy to a stricter one. Users
were annoyed by this change. However, they did feel more
secure under the new policy.
Some users struggle to comply with new password
requirements. Most users created their new password in a
single attempt, and believed they would be able to login in
one try. However, 19% of users reported already forgetting
their new passwords. These users seem to have struggled
with the policy change. They required significantly more at-
tempts to create their new passwords, believed that logging
in would require more attempts, were more annoyed, found
creating the new password less fun, and were more likely to
write their password down. Over 10% of these users went to
the help desk because they forgot their passwords.
Users are more likely to share and reuse their pass-
words than to write them down. Much has been made
of users writing down passwords. However, we found fewer
Andrew users writing down their old or new passwords than
would be expected based on previous work [16, 24]. On the
other hand, over a quarter of our respondents reported they
have shared a new or old Andrew password, and over three
quarters of users reuse passwords. Despite the fact that some
experts advocate writing passwords down as a mechanism to
cope with numerous passwords [18], reusing passwords seems
to be much more prevalent than writing them down.
Users tend to modify old passwords to create new
ones. About half our respondents reported modifying their
old password to create their new one. Others modified or
reused a password for another account. This suggests that
many users avoid creating completely new passwords when
forced to change. Since the new passwords were required
to include character classes not previously required, some
users may have added additional characters to their previous
password. This may explain why the average length of new
passwords was over 2 characters longer than required by
Users are more likely to share their passwords over
time. Around 25% of users reported having shared their old
password with at least one person. This percentage is more
than twice the percentage of users who have shared new
passwords (12%). This behavior may be associated with
the fact that, as time passes, users face situations where
they need to share their passwords, such as when they need
someone else to help them access their accounts when out
of the office. This suggests that a password policy requir-
ing a periodic change of passwords could help to protect
users’ passwords. However, the impact of such a policy on
password composition, and other aspects of user behavior,
should also be considered.
Use of dictionary words and names are still the
most common strategies to create passwords. Nearly
80% of users based their password on a word or name, with
special characters added to the beginning or end. This is
despite the new policy implementing a basic check for dic-
tionary words.
Our results reveal flaws in NIST’s assumptions.
NIST bases its per-password entropy estimates on several as-
sumptions that are inconsistent with our findings [2]. They
assume users will create passwords of the minimum required
length, but our results show an average length more than two
characters above the minimum. NIST also assumes users
will have the minimum number of special characters, but
our participants frequently indicated using more. Over two-
thirds of users who responded said they used more than the
one required number. It would be useful to examine larger
sets of passwords created under a variety of password policies
to provide empirical data to improve the NIST guidelines.
The authors wish to acknowledge and thank the staff of
the CMU Information Security Office for explaining the mo-
tivations behind the password policy change, and details of
its implementation. Particular thanks goes to Mary Ann
Blair, Doug Markiewicz, and Mark Poepping. The authors
wish to thank Veda Mujumdar and Nethra Krishnamoorthy
for their help entering survey data. The research was spon-
sored in part by NSF IGERT grant #DGE-0903659 and by
Carnegie Mellon CyLab under Army Research Office grant
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write_down_your.html, June 2005.
[19] C. E. Shannon. A mathematical theory of
communication. ACM SIGMOBILE Mobile
Computing and Communications Review, 5(1), 1949.
[20] R. Shay and E. Bertino. A comprehensive simulation
tool for the analysis of password policies. International
Journal of Information Security, 8(4):275–289, 2009.
[21] R. Shay, A. Bhargav-Spantzel, and E. Bertino.
Password policy simulation and analysis. In ACM
workshop on Digital identity management, pages 1–10,
[22] W. C. Summers and E. Bosworth. Password policy:
the good, the bad, and the ugly. In Winter
international synposium on Information and
communication technologies, pages 1–6, 2004.
[23] K.-P. L. Vu, R. W. Proctor, A. Bhargav-Spantzel,
B.-L. B. Tai, and J. Cook. Improving password
security and memorability to protect personal and
organizational information. International Journal of
Human-Computer Studies, 65(8):744–757, 2007.
[24] M. Zviran and W. J. Haga. Password security: an
empirical study. Journal of Management Information
Systems, 15(4):161–185, 1999.
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13. How'long'is'your'current'password'(total'number'of'characters)?'______'' ___I'prefer'not'to'answer'
14. What'symbols'(characters'other'than'letters'and'numbers)'are'in'your'pass w o r d ? '__ ___ _'' ___I'prefer'no t'to'an sw er'
15. How'many'lowerBcase'letters'are'in'your'current'password?'_____' ' ___I'prefer'not'to'answ er'
16. How'many'upperBcase'letters'are'in'your'current'password?'_____' ' ___I'prefer'not'to'a nswer'
17. In'which'positio n s'i n 'y o u r'p a s sword'are 'the'numbers?' ___'I'prefer'not'to'answe r'
___'First''' ___'Second' ___'Second'from'last' ___'Last' ' ___'Other' '
18. In'which'positio n s'i n 'y o u r'p a s sword'are 'the'symb o ls ?' ___'I'prefer'not'to'answe r'
___'First''' ___'Second' ___'Second'from'last' ___'Last' ' ___'Other'
19. How'many'tries'did'it'take'to'create'a'password'the'system'accepted?'_______'
20. Have'you'written'down'your'current'password?''
___'No' ''''''___'Yes,'on'paper'' ___'Yes,'ele c tr o n ic al ly '(s to r e d 'in 'c o mputer,'ph o ne,'etc.)''''''___'O t h er '__ __ __ ___ __ ___ __ __ __'
If'you'wrote'dow n 'yo ur'current'password''how'is'it'protected'(choose'all'that'a p p ly ) ?'
___'I'do'not'prote ct'it' ' ___'I'stored'it'in'an'encrypted 'file'
___'I'hid'it' ' ' ___'I'stored'it'on'a'com puter'or'device'protected 'with'anoth er'passw ord'
___'I'locke d 'u p 'th e 'p a p e r '
___'I'always'ke ep 'the'pa ssw o rd'w ith'm e '
___'I'wrot e 'd o wn'a'rem in d e r'i n st e ad 'of'the'actu a l'p a ss word'
21. Have'you'written'down'your'old'passw ord?''
___'No ' ''''''___'Yes,'on'pa pe r'' ___'Yes,'electronically'(s to r e d 'in 'c o mputer,'p h o n e ,'et c.) ''''''___ 'O t h e r'_ ___ __ ___ __ __ ___ __ __'
If'you'wrote'dow n 'yo ur'o ld 'p a s sword'ho w'is'it'prote c te d'( ch oose'all'th at 'a p p ly ) ?'
___'I'do'not'prote ct'it' ' ___'I'stored'it'in'an'encrypted 'file'
___'I'hid'it' ' ' ___'I'store d'it'on 'a'com p u ter'or'de vice 'pro tecte d'w ith'an oth er'pa ssw o rd'
___'I'locke d 'u p 'th e 'p a p e r '
___'I'always'ke ep 'the'password'with'me'
___'I'wrot e 'd o wn'a'rem in d e r'i n st e ad 'of'the'actu a l'p a ss word'
22. Do'you'have'a'set'of'passwords'you'reu s e 'in 'd iffe r e n t'p la c e s? ' ___Yes'' ___N o '
23. Do'you'have'a'password'that'you'use'for'different'accounts'with'a'slight'modification'for'each'account?''
___Yes'' ___No'
24. With'the'new'password'requirements,'my'Andrew'account'is'more'secure.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagree ' ___'Strongly'disagree '
25. Creating'a'password'that'meets'the'ne w 'requirements'was'anno ying.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagr ee ' ___'Strongly'disagree '
26. Creating'a'password'that'meets'the'new'requirements'was'difficult.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagr ee ' ___'Strongly'disagree '
27. Creating'a'password'that'meets'the'new'requirements'was'fun.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagree' ___'Strongly'disagree'
28. Any'added'prote c tio n 'provided 'b y 'th e 'n e w'passw o rd'is'worth 't h e'a d d e d 'e ffo r t 'o f'cr e a tin g / r ememb er in g / u s in g 'it.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagr ee ' ___'Strongly'disagree '
29. I'would'like'Andrew 'to'go'b ac k'to'the 'old'pa ssw o rd'p olicy .'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagree' ___'Strongly'disagree '
13. How'long'is'your'current'password'(total'number'of'characters)?'______'' ___I'prefer'not'to'answer'
14. What'symbols'(characters'other'than'letters'and'numbers)'are'in'your'password?'______'''''' '___I'prefer'not'to'answer'
15. How'many'symbols'(characters'other'than'letters'and'numbers)'are'in'your'current'password?'_____' '
16. How'many'numbers'are'in'y o u r 'current'password?'_____' ' ___I'prefer'not'to'an sw er'
17. In'which'positio n s'i n 'y o u r'p a s sword'are 'the'capital'letters?' ___'I'prefer'not'to'an sw e r'
___'First''' ___'Second' ___'Second'from'last' ___'Last' ' ___'Other' '
18. Does'your'password'contain'a'number'related'to'the'year'it'was'created?' '
___'Yes' '___'No ' ___'I'prefer 'not'to 'answ e r'
19. How'many'tries'did'it'take'to'create'a'password'the'system'accepted?'_______'
20. Have'you'written'down'your'current'password?''
___'No' ''''''___'Yes,'on'paper'' ___'Yes,'electron ica lly'(store d'in'co m pu ter ,'phon e,'etc.)''''''___'Other'____________________'
If'you'wrote'dow n 'yo ur'current'password'how'is'it'protected'(choose'all'tha t'a p p ly ) ?'
___'I'do'not'prote ct'it' ' ___'I'stored'it'in'an'encrypted'file'
___'I'hid'it' ' ' ___'I'store d'it'on 'a'com p u ter'or'de vice 'pro tecte d'w ith'an oth er'pa ssw o rd'
___'I'locke d 'u p 'th e 'p a p e r '
___'I'always'ke ep 'the'pa ssw o rd'w ith'm e '
___'I'wrot e 'd o wn'a'rem in d e r'i n st e ad 'of'the'actu a l'p a ss word'
21. Have'you'written'down'your'old'passw ord?''
___'No' ''''''___'Yes,'on'paper'' ___'Yes,'electron ica lly'(store d'in'co m pu ter ,'phon e,'etc.)''''''___'Other'____________________'
If'you'wrote'dow n 'yo ur'o ld'password'how'is'it'protected'(choose'a ll 'th a t'a p p l y) ?'
___'I'do'not'prote ct'it' ' ___'I'stored'it'in'an'encrypted 'file'
___'I'hid'it' ' ' ___'I'store d'it'on 'a'com p u ter'or'de vice 'pro tecte d'w ith'an oth er'pa ssw o rd'
___'I'locke d 'u p 'th e 'p a p e r '
___'I'always'ke ep 'the'password'with'me'
___'I'wrot e 'd o wn'a'rem in d e r'i n st e ad 'of'the'actu a l'p a ss word'
22. Do'you'have'a'set'of'passwords'you'reu s e 'in 'd iffe r e n t'p la c e s? ' ___Yes'' ___N o '
23. Do'you'have'a'password'that'you'use'for'different'accounts'with'a'slight'modification'for'each'account?''
___Yes'' ___No'
24. With'the'new'password'requirements,'my'Andrew'account'is'more'secure.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagree ' ___'Strongly'disagree '
25. Creating'a'password'that'meets'the'ne w 'requirements'was'anno ying.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagr ee ' ___'Strongly'disagree '
26. Creating'a'password'that'meets'the'new'requirements'was'easy.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagr ee ' ___'Strongly'disag ree '
27. Creating'a'password'that'meets'the'new'requirements'was'fun.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagr ee ' ___'Strongly'disagree '
28. Any'added'prote c tio n 'provided 'b y 'th e 'n e w'passw o rd'is'worth 't h e'a d d e d 'e ffo r t 'o f'cr e a tin g / r ememb er in g / u s in g 'it.'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagr ee ' ___'Strongly'disagree '
29. I'would'like'Andrew 'to'go'b ac k'to'the 'old'pa ssw o rd'p olicy .'
___'Strongly'agree' ___'Agree' ___'Neutral' ___'Disagree' ___'Strongly'disagree '
This survey is filled in with the responses we received.
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... To increase the likelihood of participants engaging with the study, and treating the passwords as they were for real accounts; a technique was employed that has been used many times before with success: role-play (e.g. Forget, Chiasson, and Biddle 2008;Gaw and Felten 2006;Komanduri et al. 2011;Shay et al. 2010;Woods and Siponen 2018). The participants were asked to roleplay, and imagine they were creating and recalling passwords for real accounts, even though they were aware they were fictious. ...
... Therefore, the participants were asked to role-play, a technique used in many previous studies (e.g. Forget, Chiasson, and Biddle 2008;Gaw and Felton, 2006;Komanduri et al. 2011;Shay et al. 2010Shay et al. , 2015Shay et al. , 2016Woo et al. 2019). It was stipulated before the study began, 'We would appreciate it if you would learn your passwords to the best of your ability, and consider them as protecting "real" accounts, as we will monitor your interaction with the system'. ...
The authentication process is the first line of defence against potential impostors, and therefore is an important concern when protecting personal and organisational data. Although there are many options to authenticate digital users, passwords remain the most common authentication mechanism. However, with password numbers increasing, many users struggle with remembering multiple passwords, which affects their security behaviour. Previous researchers and practitioners have attempted to suggest ways to improve password memorability and security simultaneously. We introduce novel approach that utilises colour as a memory cue to increase password memorability and security. A longitudinal study examined in total over 3000 passwords that were created, learnt and recalled (password process) over a period of five-weeks. By adding colour to the password process, our results suggest that password memorability and security can be increased simultaneously. Through giving the user the option of choosing the colours (compared with colours being preselected), encourages users to create more personal and meaningful memory cues when creating their passwords. Additionally, colour also provided another security parameter by increasing password entropy. These unique results have practical implications for researchers and practitioners that could positively impact password security, and the financial losses suffered due to password security breaches.
... It poses the question: What are users thinking when creating passwords?. A survey of 470 Carnegie Mellon University computer users collected data on the behaviours and practices related to password use and creation when faced with new stricter password policies [10]. Results showed 19% of users had difficulty remembering passwords, and that over half the users reused or slightly modified old passwords, often with the inclusion of special characters, as they were a new policy requirement. ...
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Passwords are still the most common authentication method for various digital services. The majority of the research into passwords is focused on technical concerns rather than the human elements of password construction. In this paper, we aim at studying cultural aspects of leaked passwords with the usage of an online game. In particular, we introduce a novel web-based data collection tool utilizing gamification elements that benefits from appealing aesthetics and implemented narrative elements to engage users into prolonged play. The player's role is to label presented passwords with available descriptive tags. Our goal is to collect a large number of gaming data to identify prevalent tag choices through consensus, and as such, assign perceived meaning to the passwords through the tags. An initial field test of the prototype returned a high number of responses that were determined to be valid when assessed via internal controls.
... These were quite useful for early systems that had small password length limits. With the development of information systems, password-generation methods based on password dictionaries and heuristic rules [29] were proposed and became the popular methods for guessing a password. Later, a password-generation model based on the Markov process appeared [16]. ...
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With the development of the Internet, information security has attracted more attention. Identity authentication based on password authentication is the first line of defense; however, the password-generation model is widely used in offline password attacks and password strength evaluation. In real attack scenarios, high-probability passwords are easy to enumerate; extremely low-probability passwords usually lack semantic structure and, so, are tough to crack by applying statistical laws in machine learning models, but these passwords with lower probability have a large search space and certain semantic information. Improving the low-probability password hit rate in this interval is of great significance for improving the efficiency of offline attacks. However, obtaining a low-probability password is difficult under the current password-generation model. To solve this problem, we propose a low-probability generator-probabilistic context-free grammar (LPG-PCFG) based on PCFG. LPG-PCFG directionally increases the probability of low-probability passwords in the models' distribution, which is designed to obtain a degeneration distribution that is friendly for generating low-probability passwords. By using the control variable method to fine-tune the degeneration of LPG-PCFG, we obtained the optimal combination of degeneration parameters. Compared with the non-degeneration PCFG model, LPG-PCFG generates a larger number of hits. When generating 107 and 108 times, the number of hits to low-probability passwords increases by 50.4% and 42.0%, respectively.
... More specifically, Das et al. [20] show that an attacker who knows one password of a user can leverage this knowledge to guess their passwords on other sites, while Wang et al. [52] show that a targeted attacker can benefit from a user's personal information (e.g., name, birthday) to guess their passwords. Shay et al. [43] also show that many users choose to modify their existing passwords when faced with a change in password policy. Our results confirm and expand on these results, demonstrating for the first time a targeted online attack for guessing 6-digit PINs based on previously selected 4-digit PINs. ...
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With the goal of improving security, companies like Apple have moved from requiring 4-digit PINs to 6-digit PINs in contexts like smartphone unlocking. Users with a 4-digit PIN thus must "upgrade" to a 6-digit PIN for the same device or account. In an online user study (n = 1010), we explore the security of such upgrades. Participants used their own smart-phone to first select a 4-digit PIN. They were then directed to select a 6-digit PIN with one of five randomly assigned justifications. In an online attack that guesses a small number of common PINs (10-30), we observe that 6-digit PINs are, at best, marginally more secure than 4-digit PINs. To understand the relationship between 4-and 6-digit PINs, we then model targeted attacks for PIN upgrades. We find that attackers who know a user's previous 4-digit PIN perform significantly better than those who do not at guessing their 6-digit PIN in only a few guesses using basic heuristics (e.g., appending digits to the 4-digit PIN). Participants who selected a 6-digit PIN when given a "device upgrade" justification selected 6-digit PINs that were the easiest to guess in a targeted attack, with the attacker successfully guessing over 25% of the PINs in just 10 attempts, and more than 30% in 30 attempts. Our results indicate that forcing users to upgrade to 6-digit PINs offers limited security improvements despite adding usability burdens. System designers should thus carefully consider this tradeoff before requiring upgrades.
... Researchers looked at users behavior when registering and using passwords. Shay et al. [46] show that more than half of participants modify an old password or reuse a password when signing up. Von Zezschwitz et al. [51] found through user interviews that 45% of users reuse the exact passwords. ...
Human-chosen passwords are the dominant form of authentication systems. Passwords strength estimators are used to help users avoid picking weak passwords by predicting how many attempts a password cracker would need until it finds a given password. In this paper we propose a novel password strength estimator, called PESrank, which accurately models the behavior of a powerful password cracker. PESrank calculates the rank of a given password in an optimal descending order of likelihood. PESrank estimates a given password’s rank in fractions of a second – without actually enumerating the passwords – so it is practical for online use. It also has a training time that is drastically shorter than previous methods. Moreover, PESrank is efficiently tweakable to allow model personalization in fractions of a second, without the need to retrain the model; and it is explainable: it is able to provide information on why the password has its calculated rank, and gives the user insight on how to pick a better password. We implemented PESrank in Python and conducted an extensive evaluation study of it. We also integrated it into the registration page of a course at our university. Even with a model based on 905 million passwords, the response time was well under 1 second, with up to a 1-bit accuracy margin between the upper bound and the lower bound on the rank.
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"We're secure! We use passwords!" How many of us have heard this claim? Or even -- "We're secure! We have a password policy!" Using a password or having a password policy in today's world of computing is not enough. Passwords provide a first line of defense in most cases, but there is much more. "A recent survey by Rainbow Technologies Inc. indicates that the use of insecure passwords can be costly -- and potentially risky -- for corporate data. "[Rosencrance] This paper focuses on the use of passwords and password policy and looks at the good, the bad and the ugly scenarios that arise.
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Modern organizations rely on passwords for preventing illicit access to valuable data and resources. A well designed password policy helps users create and manage more effective passwords. This paper offers a novel model and tool for understanding, creating, and testing password policies. We present a password policy simulation model which incorporates such factors as simulated users, accounts, and services. This model and its implementation enable administrators responsible for creating and managing password policies to test them before giving them to actual users. It also allows researchers to test how different password policy factors impact security, without the time and expense of actual human studies. We begin by presenting our password policy simulation model. We next discuss prior work and validate the model by showing how it is consistent with previous research conducted on human users. We then present and discuss experimental results derived using the model.
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HCI research published 10 years ago pointed out that many users cannot cope with the number and complexity of passwords, and resort to insecure workarounds as a consequence. We present a study which re-examined password policies and password practice in the workplace today. 32 staff members in two organisations kept a password diary for 1 week, which produced a sample of 196 passwords. The diary was followed by an interview which covered details of each password, in its context of use. We find that users are in general concerned to maintain security, but that existing security policies are too inflexible to match their capabilities, and the tasks and contexts in which they operate. As a result, these password policies can place demands on users which impact negatively on their productivity and, ultimately, that of the organisation. We conclude that, rather than focussing password policies on maximizing password strength and enforcing frequency alone, policies should be designed using HCI principles to help the user to set an appropriately strong password in a specific context of use.
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Previous efforts involving picture-based passwords have not fo- cused on maintaining a measurably high level of entropy. Since password systems usually allow user selection of passwords, their true entropy remains unknown. A 23-participant study was per- formed in which picture and character-based passwords of equal strength were randomly assigned. Memorability was tested with up to one week between sessions. The study found that both char- acter and picture passwords of very high entropy were easily for- gotten. However, when password inputs were analyzed to deter- mine the source of input errors, serial ordering was found to be the main cause of failure. This supports a hypothesis stating that picture-password systems which do not require ordered input may produce memorable, high-entropy passwords. Input analysis pro- duced another interesting result, that incorrect inputs by users are often duplicated. This reduces the number of distinct guesses users can make when authentication systems lock out users after a num- ber of failed logins. A protocol for ignoring duplicate inputs is presented here. A shoulder-surfing resistant input method was also evaluated, with six out of 15 users performing an insecure behav- ior.
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Passwords are an ubiquitous and critical component of many security systems. As the information and access guarded by passwords become more necessary, we become ever more dependent upon the security passwords provide. The creation and management of passwords is crucial, and for this we must develop and deploy password policies. This paper focuses on defining and modeling password policies for the entire password policy lifecycle. The paper first discusses a language for specifying password policies. Then, a simulation model is presented with a comprehensive set of variables and the algorithm for simulating a password policy and its impact. Finally, the paper presents several simulation results using the password policy simulation tool.
Bell System Technical Journal, also pp. 623-656 (October)
Passwords are the core authentication method behind most computer security systems. This paper presents a summary of the responses of students to a survey on password practices and attitudes. The paper also presents the results of an experiment to determine the effect of a brief presentation of password security best practices on students.
In developing password policies, IT managers must strike a balance between security and memorability. Rules that improve structural integrity against attacks may also result in passwords that are difficult to remember. Recent technologies have relaxed the 8-character password constraint to permit the creation of longer pass-“phrases” consisting of multiple words. Longer passphrases are attractive because they can improve security by increasing the difficulty of brute-force attacks and they might also be easy to remember. Yet, no empirical evidence concerning the actual usability of passphrases exists. This paper presents the results of a 12-week experiment that examines users’ experience and satisfaction with passphrases. Results indicate that passphrase users experienced a rate of unsuccessful logins due to memory recall failure similar to that of users of self-generated simple passwords and stringent passwords. However, passphrase users had more failed login attempts due to typographical errors than did users of either simple or highly secure passwords. Moreover, although the typographical errors disappeared over time, passphrase users’ initial problems negatively affected their end-of-experiment perceptions.
Personal information and organizational information need to be protected, which requires that only authorized users gain access to the information. The most commonly used method for authenticating users who attempt to access such information is through the use of username–password combinations. However, this is a weak method of authentication because users tend to generate passwords that are easy to remember but also easy to crack. Proactive password checking, for which passwords must satisfy certain criteria, is one method for improving the security of user-generated passwords. The present study evaluated the time and number of attempts needed to generate unique passwords satisfying different restrictions for multiple accounts, as well as the login time and accuracy for recalling those passwords. Imposing password restrictions alone did not necessarily lead to more secure passwords. However, the use of a technique for which the first letter of each word of a sentence was used coupled with a requirement to insert a special character and digit yielded more secure passwords that were more memorable.
As the Internet has grown, its user community has changed from a small tight-knit group of researchers to a loose gathering of people on a global network. The amazing and constantly growing numbers of machines and users ensures that untrustworthy individuals have full access to that network. High speed inter-machine communication and even higher speed computational processors have made the threats of system “crackers”, data theft, and data corruption very real. This paper outlines some of the problems of current password security by demonstrating the ease by which individual at counts may be broken. Various techniques used by crackers are outlined, and finally one solution to this point of system vulnerability, a proactive password checker, is documented.