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JonDonym Users’ Information Privacy Concerns

JonDonym Users’ Information Privacy Concerns
David Harborth1[0000000195547567] and
Sebastian Pape1(B)[0000000208937856]
Chair of Mobile Business and Multilateral Security
Goethe University, Frankfurt, Germany
Abstract.
Privacy concerns as well as trust and risk beliefs are im-
portant factors that can influence users’ decision to use a service. One
popular model that integrates these factors is relating the Internet Users
Information Privacy Concerns (IUIPC) construct to trust and risk beliefs.
However, studies haven’t yet applied it to a privacy enhancing technology
(PET) such as an anonymization service. Therefore, we conducted a
survey among 416 users of the anonymization service JonDonym [
1
] and
collected 141 complete questionnaires. We rely on the IUIPC construct
and the related trust-risk model and show that it needs to be adapted
for the case of PETs. In addition, we extend the original causal model by
including trust beliefs in the anonymization service provider and show
that they have a significant effect on the actual use behavior of the PET.
Keywords:
Internet Users’ Information Privacy Concerns, IUIPC, Anonymity
Services, Privacy Concerns, Trust Beliefs, Risk Beliefs
1 Introduction
Privacy concerns have been discussed since the very beginning of computer shar-
ing [
2
]. With a raising economic interest in the internet [
3
], they gain importance.
Bruce Schneier [
4
] states: “Surveillance is the business model of the internet.
Everyone is under constant surveillance by many companies, ranging from social
networks like Facebook to cellphone providers.” Thus, it can not be a surprise
that users have privacy concerns and feel a strong need to protect their privacy
1
.
One popular model for measuring and explaining privacy concerns of online
users is the Internet Users Information Privacy Concerns (IUIPC) construct
by Malhotra et al. [
6
]. Their research involves a theoretical framework and an
instrument for operationalizing privacy concerns, as well as a causal model for
this construct including trust and risk beliefs about the online companies’ data
handling of personal information. The IUIPC construct has been used in various
contexts, e.g. Internet of Things [
7
], internet transactions [
8
] and Mobile Apps [
9
],
but to the best of our knowledge the IUIPC construct has never been applied to a
privacy enhancing technology (PET) such as anonymization services. The IUIPC
1
“The mean value for the statement ‘I feel very strongly about protecting my privacy’
was 3.64 on a five-point scale with no statistically significant differences across gender,
income groups, educational levels, or political affiliation.” [5]
instrument shows its strengths best when a service with a certain use for the
customer (primary use) is investigated with respect to privacy concerns. However,
for anonymization services the primary purpose is to help users to protect their
privacy. As a consequence, it is necessary to distinguish between trust and risk
beliefs with respect to technologies which aim to protect personal (PETs) and
regular internet services. Therefore, the trust model within IUIPC’s causal model
needs to be adapted for the investigation of anonymization services. For that
purpose, we conducted a survey among 416 users of the anonymization service
JonDonym [
1
] and collected 141 complete questionnaires. Our results contribute
to the understanding of users’ perceptions about PETs and indicate how privacy
concerns and trust and risk beliefs influence the use behavior of PETs.
The remainder of the paper is structured as follows: Sect. 2 briefly introduces
the JonDonym anonymization service and lists related work on PETs. In section
3, we present the research hypotheses and describe the questionnaire and the
data collection process. We assess the quality of our empirical results with regard
to reliability and validity in section 4. In section 5, we discuss the implications of
the results, elaborate on limitations of the framework and conclude the paper
with suggestions for future work.
2 Background and Related Work
Privacy-Enhancing Technologies (PETs) is an umbrella term for different privacy
protecting technologies. Borking and Raab define PETs as a “coherent system of
ICT measures that protects privacy [...] by eliminating or reducing personal data
or by preventing unnecessary and/or undesired processing of personal data; all
without losing the functionality of the data system” [10, S. 1].
In this paper, we investigate the privacy, trust and risk beliefs associated with
PETs for the case of the anonymity service JonDonym [
1
]. Comparable to Tor,
JonDonym is an anonymity service. However, unlike Tor, it is a proxy system
based on mix cascades. It is available for free with several limitations, like the
maximum download speed. In addition, there are different premium rates without
these limitations that differ with regard to duration and included data volume.
Thus, JonDonym offers several different tariffs and is not based on donations
like Tor. The actual number of users is not predictable since the service does not
keep track of this. JonDonym is also the focus of an earlier user study on user
characteristics of privacy services [
11
]. However, the focus of the study is rather
descriptive and does not focus on users’ beliefs and concerns.
Previous non-technical work on PETs considers mainly usability studies and
does not primarily focus on privacy concerns and related trust and risk beliefs of
PET users. For example, Lee et al. [
12
] assess the usability of the Tor Launcher
and propose recommendations to overcome the found usability issues. Benenson
et al. [
13
] investigate acceptance factors for anonymous credentials. Among other
things, they find that trust in the PET has no statistically significant impact on
the intention to use the service. This result is relevant for our study since we also
hypothesize that trust in JonDonym has a positive effect on the actual use of
the service (see Section 3.1). Janic et al. [
14
] claim to consider the relationship
between privacy concerns, transparency enhancing technologies (TETs) and
PETs, but have a strong focus on TETs and only provide a literature review.
3 Methodology
We base our research on the Internet Users Information Privacy Concerns (IUIPC)
model by Malhotra et al. [
6
]. The original research on this model investigates the
role of users’ information privacy concerns in the context of releasing personal
information to a marketing service provider. Since we want to investigate the
role of privacy concerns, trust and risk beliefs for using a PET (i.e. JonDonym),
we can adapt the model by substituting the behavioral intention to perform an
action with the actual use of JonDonym. This is possible since we asked current
users of JonDonym who actively use the PET. In addition, we extend the original
model by trusting beliefs in the PET itself. We argue that the level of trust in a
PET is a crucial factor determining the use decision.
For analyzing the cause-effect relationships between the latent (unobserved)
variables, we use structural equation modelling (SEM). There are two main
approaches for SEM, namely covariance-based SEM (CB-SEM) and partial
least squares SEM (PLS-SEM) [
15
]. Since our research goal is to predict the
target construct actual use behavior of JonDonym, we use PLS-SEM for our
analysis [
15
,
16
]. In the following subsections, we discuss the hypotheses based
on the IUIPC model [6], the questionnaire and the data collection process.
3.1 Research Hypotheses
As Figure 1 shows, the structural model contains several relationships between
exogenous and endogenous variables. We develop our research hypotheses for
these relationships based on the original hypotheses of the IUIPC model [
6
]. In
the original article, IUIPC is operationalized as a second-order construct of the
sub-constructs collection (COLL), awareness (AWA) and control (CONTROL)
2
.
Thus, the privacy concerns of users are determined by their concerns about
“[...]individual-specific data possessed by others relative to the value of benefits
receive” [
6
, p. 338], the control they have over their own data (i.e. possibilities to
change or opt-out) and the “[...] degree to which a consumer is concerned about
his/her awareness of organizational information privacy practices” [6, p. 339].
The effect of IUIPC on the behavioral intention (in our model the actual
use behavior) is moderated by trusting beliefs and risk beliefs. Trusting beliefs
represent users’ perceptions about the behavior of online firms to protect the
users’ personal information. In contrast, risk beliefs represent users’ perception
about losses associated with providing personal data to online firms [
6
]. Thus, the
higher the privacy concerns of a user, the lower are his or her trusting beliefs and
2
Due to space limitations, we will not elaborate on the statistics of second-order
constructs here. For an extensive discussion see Steward and Malhotra [17, 6].
the higher are his or her risk beliefs. In addition, a higher level of trust is assumed
to decrease the risk beliefs. Thus, we derive the following three hypotheses:
H 1:
Internet Users Information Privacy Concerns (IUIPC) have a negative
effect on Trusting Beliefs (TB).
H 2:
Internet Users Information Privacy Concerns (IUIPC) have a positive
effect on Risk Beliefs (RB).
H 3: Trusting Beliefs (TB) have a negative effect on Risk Beliefs (RB).
Since we investigate the use of a specific PET, JonDonym, we extend the model
by including the trust of users in JonDonym itself. For that purpose, we adapt the
trust construct by Pavlou [
18
]. However, in order to protect their privacy, users
with higher privacy concerns are assumed to rather trust the privacy-enhancing
technology compared to online firms that process personal data. In particular,
because we surveyed users of the PET. Therefore, we hypothesize:
H 4:
Internet Users Information Privacy Concerns (IUIPC) have a positive
effect on the trusting beliefs in JonDonym (T BJ D ).
Trust is an important factor in the acceptance decision of users [
18
]. Especially
for the case of privacy protection, we assume that trust in JonDonym is a major
factor in the decision to use the technology. Thus, we hypothesize that:
H 5:
Trusting beliefs in JonDonym (
T BJD
) have a positive effect on the actual
use behavior of JonDonym (USE).
When considering the effects of trusting and risk beliefs on behavior in the
context of releasing data to online companies, it is logical that trusting beliefs have
a positive effect and risk beliefs have a negative effect on releasing data. However,
in our case with actual use behavior of a PET, we assume these effects reverse.
The higher the trusting beliefs in online firms, the lower is the use frequency of
JonDonym, since the protection of data becomes less important. Following this
rationale, a higher degree of risk beliefs with respect to the data processing of
online firms leads to a higher degree of use. Therefore, we hypothesize that:
H 6:
Trusting beliefs (TB) have a negative effect on actual use behavior of
JonDonym (USE).
H 7:
Risk beliefs (RB) have a positive effect on actual use behavior of JonDonym
(USE).
3.2 Questionnaire Composition and Data Collection Procedure
The questionnaire constructs are adapted from the original IUIPC paper [
6
]. We
conducted the study with German and English speaking JonDonym users. Thus,
we administered two questionnaires. All items for the German questionnaire had
to be translated into German since all of the constructs are adapted from English
literature. To ensure content validity of the translation, we followed a rigorous
translation process [
19
,
20
]. First, we translated the English questionnaire into
German with the help of a certified translator (translators are standardized
following the DIN EN 15038 norm). The German version was then given to a
second independent certified translator who retranslated the questionnaire to
English. This step was done to ensure the equivalence of the translation. Third, a
group of five academic colleagues checked the two English versions with regard to
this equivalence. All items were found to be equivalent. The items of the English
version can be found in Appendix A.
Since we investigate the effect of privacy concerns, trust and risk beliefs
on the use of JonDonym, we collected data of actual users of the PET. We
installed the surveys on a university server and managed it with the survey
software LimeSurvey (version 2.63.1) [
21
]. The links to the English and German
version were distributed with the beta version of the JonDonym browser and
published on the official JonDonym homepage. In sum, 416 participants started
the questionnaire (173 for the English version and 243 for the German version).
Of those 416 approached participants, 141 (53 for the English version and 88 for
the German version) remained after deleting unfinished sets and all participants
who answered a test question in the middle of the survey incorrectly.
The demographic questions were not mandatory to fill out. This was done
on purpose since we assumed that most of the participants are highly sensitive
with respect to their personal data. Therefore, we resign from a discussion of
the demographics in our research context. This decision is backed up by Singh
and Hill, who found no statistically significant differences across gender, income
groups, educational levels, or political affiliation in the desire to protect one’s
privacy [5].
4 Results
We tested the model using SmartPLS version 3.2.6 [
22
]. Before looking at the
result of the structural model and discussing its implications, we discuss the
measurement model, and check for the reliability and validity of our results. This
is a precondition of being able to interpret the results of the structural model.
Furthermore, it is recommended to report the computational settings. For the
PLS algorithm, we choose the path weighting scheme with a maximum of 300
iterations and a stop criterion of 10
7
. For the bootstrapping procedure, we use
5000 bootstrap subsamples and no sign changes as the method for handling sign
changes during the iterations of the bootstrapping procedure.
4.1 Assessment of the Measurement Model
As the model is measured solely reflectively, we need to evaluate the internal
consistency reliability, convergent validity and discriminant validity to assess the
measurement model properly [15].
Table 1.
Loadings and Cross-Loadings of the Reflective Items and Internal Consistency
Reliability
Constructs AWA CONTROL COLL RB TB TBJD IUIPC USE
AWA1 0.892 0.254 0.297 0.050 -0.107 0.073 0.614 0.143
AWA2 0.927 0.254 0.287 0.072 -0.152 0.057 0.622 0.098
AWA3 0.883 0.297 0.356 0.235 -0.207 0.071 0.648 0.169
CONTROL1 0.284 0.837 0.379 0.271 -0.306 0.163 0.618 0.208
CONTROL2 0.244 0.808 0.238 0.205 -0.075 0.103 0.505 0.175
CONTROL3 0.201 0.819 0.348 0.287 -0.195 0.089 0.514 0.138
COLL1 0.202 0.309 0.781 0.237 -0.084 0.152 0.588 0.133
COLL2 0.199 0.185 0.760 0.141 0.001 0.262 0.548 0.300
COLL3 0.380 0.364 0.873 0.192 -0.063 0.297 0.733 0.302
COLL4 0.336 0.416 0.872 0.349 -0.213 0.193 0.720 0.261
RB1 0.117 0.213 0.230 0.814 -0.324 0.022 0.194 0.157
RB2 0.061 0.172 0.100 0.710 -0.201 -0.114 0.116 0.050
RB3 0.132 0.225 0.193 0.815 -0.179 -0.098 0.196 0.123
RB4 0.075 0.214 0.266 0.811 -0.241 -0.076 0.211 0.050
RB5 -0.112 -0.311 -0.244 -0.682 0.392 0.050 -0.277 -0.092
TB1 -0.174 -0.217 -0.078 -0.296 0.832 0.028 -0.196 -0.117
TB2 -0.114 -0.171 -0.033 -0.281 0.835 -0.101 -0.130 -0.134
TB3 -0.167 -0.210 -0.116 -0.343 0.815 0.004 -0.209 -0.024
TB4 -0.123 -0.160 -0.089 -0.212 0.666 -0.051 -0.129 -0.060
TB5 -0.121 -0.210 -0.137 -0.354 0.855 -0.158 -0.200 -0.210
TBJD1 0.017 0.104 0.244 -0.058 -0.100 0.898 0.130 0.281
TBJD2 0.088 0.117 0.222 -0.109 -0.043 0.922 0.165 0.303
TBJD3 0.090 0.176 0.284 -0.032 -0.060 0.922 0.199 0.330
IUIPC 0.698 0.669 0.794 0.276 -0.220 0.183 1.000 0.333
USE 0.152 0.214 0.304 0.130 -0.142 0.335 0.333 1.000
Cronbach’s α0.883 0.761 0.841 0.612 0.862 0.902 1.000 1.000
Composite Reliability 0.928 0.862 0.893 0.749 0.901 0.938 1.000 1.000
Internal Consistency Reliability Internal consistency reliability (ICR) measure-
ments indicate how well certain indicators of a construct measure the same latent
phenomenon. Two standard approaches for assessing ICR are Cronbach’s
α
and
the composite reliability. The values of both measures should be between 0.7
and 0.95 for research that builds upon accepted models. Values of Cronbach’s
α
are seen as a lower bound and values of the composite reliability as an upper
bound of the assessment [
16
]. Table 1 includes the ICR of the variables in the
last two rows. It can be seen that all values for Cronbach’s
α
are above the
lower threshold of 0.7 except for RB. However, for the composite reliability the
value for RB is higher than 0.7. Therefore, we argue that ICR is not an issue
for this variable. For all variables, no value is above 0.95. Values above that
upper threshold indicate that the indicators measure the same dimension of the
latent variable, which is not optimal with regard to the validity [
16
]. In sum, ICR
is established for our variables. The variables IUIPC and USE are single-item
constructs, and thus have ICR values of 1.
Table 2. Discriminant Validity with AVEs and Construct Correlations
Constructs (AVE) AWA COLL CONTROL IUIPC RB TB TBJD USE
AWA (0.811) 0.901
COLL (0.678) 0.349 0.823
CONTROL (0.675) 0.298 0.396 0.822
IUIPC (1.000) 0.698 0.794 0.669 1,000
RB (0.591) 0.134 0.284 0.311 0.276 0.769
TB (0.646) -0.173 -0.116 -0.243 -0.220 -0.377 0.804
TBJD (0.835) 0.074 0.275 0.148 0.183 -0.071 -0.072 0.914
USE (1.000) 0.152 0.304 0.214 0.333 0.130 -0.142 0.335 1.000
Note: AVEs in parentheses in the first column. Values for
AV E
are shown
on the diagonal and construct correlations are off-diagonal elements.
Convergent Validity Convergent validity determines the degree to which indicators
of a certain reflective construct are explained by that construct. This is assessed
by calculating the outer loadings of the indicators of the constructs (indicator
reliability) and by looking at the average variance extracted (AVE) [
15
]. Loadings
above 0.7 imply that the indicators have much in common, which is desirable
for reflective measurement models [
16
]. Table 1 shows the outer loadings in bold
on the diagonal. All loadings are higher than 0.7, except for RISK5 and TB5.
Since the AVE of these constructs is still above 0.5, we do not drop these items.
Convergent validity for the construct is assessed by the AVE. AVE is equal to
the sum of the squared loadings divided by the number of indicators. A threshold
of 0.5 is acceptable, indicating that the construct explains at least half of the
variance of the indicators [
16
]. The diagonal values of Table 2 present the AVE of
our constructs. All values are well above 0.5, demonstrating convergent validity.
Discriminant Validity Discriminant validity measures the degree of uniqueness of
a construct compared to other constructs. Comparable to the convergent validity
assessment, two approaches are used for investigated discriminant validity. The
first approach, assessing cross-loadings, is dealing with single indicators. All outer
loadings of a certain construct should be larger than its cross-loadings with other
constructs [
15
]. Table 1 illustrates the cross-loadings as off-diagonal elements. All
cross-loadings are smaller than the outer loadings, fulfilling the first assessment
approach of discriminant validity. The second approach is on the construct level
and compares the square root of the constructs’ AVE with the correlations with
other constructs. The square root of the AVE of a single construct should be larger
than the correlation with other constructs (Fornell-Larcker criterion) [
16
]. Table
2 contains the square root of the AVE on the diagonal in parentheses. All values
are larger than the correlations with other constructs, indicating discriminant
validity. Since there are problems in determining the discriminant validity with
both approaches, researchers propose the heterotrait-monotrait ratio (HTMT)
for assessing discriminant validity as a superior approach to the former ones [
23
].
HTMT divides between-trait correlations by within-trait correlations, therefore
providing a measure of what the true correlation of two constructs would be if
Table 3. Heterotrait-Monotrait Ratio (HTMT)
Constructs AWA COLL CONTROL IUIPC RB TB TBJD USE
AWA
COLL 0.393
CONTROL 0.360 0.478
IUIPC 0.742 0.858 0.761
RB 0.155 0.313 0.368 0.282
TB 0.198 0.142 0.287 0.232 0.402
TBJD 0.091 0.314 0.171 0.190 0.109 0.118
USE 0.161 0.330 0.242 0.333 0.133 0.146 0.351
the measurement is flawless [
16
]. Values close to 1 for HTMT indicate a lack of
discriminant validity. A conservative threshold is 0.85 [
23
]. Table 3 contains the
values for HTMT and no value is above the suggested threshold of 0.85.
To evaluate whether the HTMT statistics are significantly different from
1, a bootstrapping procedure with 5,000 subsamples is conducted to get the
confidence interval in which the true HTMT value lies with a 95% chance. The
HTMT measure requires that no confidence interval contains the value 1. The
conducted analysis shows that this is the case. Thus, discriminant validity is
established for our model.
Common Method Bias The common method bias (CMB) can occur if data is
gathered with a self-reported survey at one point in time in one questionnaire
[
24
]. Since this is the case in our research design, the need to test for CMB arises.
An unrotated principal component factor analysis is performed with the
software package STATA 14.0 to conduct the Harman’s single-factor test to
address the issue of CMB [
25
]. The assumptions of the test are that CMB is
not an issue if there is no single factor that results from the factor analysis or
that the first factor does not account for the majority of the total variance [
25
].
The test shows that six factors have eigenvalues larger than 1 which account
for 69.45% of the total variance. The first factor explains 23.74% of the total
variance. Based on the results of previous literature [
26
], we argue that CMB is
not likely to be an issue in the data set.
4.2 Assessment and Results of the Structural Model
To assess the structural model, we follow the steps proposed by Hair et al. [
16
]
which include an assessment of possible collinearity problems, of path coefficients,
of the level of
R2
, of the effect size
f2
, of the predictive relevance
Q2
and the
effect size
q2
. We address these evaluation steps to ensure the predictive power
of the model with regard to the target constructs.
Collinearity Collinearity is present if two predictor variables are highly correlated
with each other. To address this issue, we assess the inner variance inflation
factor (inner VIF). All VIF values above 5 indicate that collinearity between
constructs is present. For our model, the highest VIF is 1.179. Thus collinearity
is apparently not an issue.
Fig. 1. Path Estimates and Adjusted R2values of the Structural Model
Significance and Relevance of Model Relationships Figure 1 presents the results
of the path estimations and the adjusted
R2
of the endogenous variable USE. We
used the adjusted
R2
as it is a conservative measure for the explained variance
of a dependent variable by avoiding a bias towards more complex models [
16
].
The
R2
is 0.12 for USE. Thus, our models explains 12% of the variance in USE.
There are different proposals for interpreting the size of this value. We choose
to use the very conservative threshold proposed by Hair et al. [
15
], where
R2
values are weak with values around 0.25, moderate with 0.50 and substantial with
0.75. Based on this classification, the
R2
value for USE is rather weak. The path
coefficients are presented on the arrows connecting the exogenous and endogenous
constructs in Figure 1. Statistical significance is indicated by asterisks, ranging
from three asterisks for p-values smaller than 0.01 to one asterisk for p-values
smaller than 0.10. The p-value indicates the probability that a path estimate
is incorrectly assumed to be significant. Thus, the lower the p-value, the higher
the probability that the given relationship exists. The relevance of the path
coefficients is expressed by the relative size of the coefficient compared to the
other explanatory variables [16].
It can be seen that IUIPC has a statistically significant negative medium-sized
effect on trusting beliefs and a positive effect on risk beliefs. The effect of IUIPC
on trusting beliefs in JonDonym is significant, positive and medium-sized. The
construct trusting beliefs has a statistically significant medium-sized negative
effect on risk beliefs. The effect of trusting beliefs on use behavior is negative,
but not statistically significant. The same holds for the relationship between risk
beliefs and use behavior (for both
p
0
.
10). In contrast, the effect of trusting
beliefs in JonDonym on use behavior is highly statistically significant, positive
and large with 0.339.
Table 4. Values for the f2and q2Effect Size Assessment
Variables f2q2
Exogenous
Endogenous USE USE
RB 0.016 0.012
TB 0.005 -0.016
TBJD 0.131 0.109
Effect Sizes
f2
The
f2
effect size measures the impact of a construct on the
endogenous variable by omitting it from the analysis and assessing the resulting
change in the
R2
value [
16
]. The values are assessed based on thresholds by
Cohen [
27
], who defines effects as small, medium and large for values of 0.02,
0.15 and 0.35, respectively. Table 4 shows the results of the
f2
evaluation. Values
in italics indicate small effects and values in bold indicate medium effects. All
other values have no substantial effect. The results correspond to those of the
previous analysis of the path coefficients.
Predictive Relevance
Q2
The
Q2
measure indicates the out-of-sample predictive
relevance of the structural model with regard to the endogenous latent variables
based on a blindfolding procedure [
16
]. We used an omission distance d=7.
Recommended values for d are between five and ten [
15
]. Furthermore, we report
the
Q2
values of the cross-validated redundancy approach, since this approach is
based on both the results of the measurement model as well as of the structural
model [
16
]. Detailed information about the calculation cannot be provided due
to space limitations. For further information see Chin [
28
]. For our model,
Q2
is
calculated for USE. Values above 0 indicate that the model has the property of
predictive relevance. In our case, the
Q2
value is equal to 0.097 for USE. Since
they are larger than 0, predictive relevance of the model is established.
Effect Sizes
q2
The assessment of
q2
follows the same logic as the one of
f2
. It is
based on the
Q2
values of the endogenous variables and calculates the individual
predictive power of the exogenous variables by omitting them and comparing the
change in Q2. The effect sizes q2have to be calculated with the formula [16]:
q2
XY=Q2
included Q2
excluded
1Q2
included
All individual values for
q2
are calculated with an omission distance d of seven.
The results are shown in Table 4. The thresholds for the
f2
interpretation can be
applied here, too [
27
]. Values in italics indicate small effects and values in bold
indicate medium effects. All other values have no substantial effect. As before,
only the trust in JonDonym has a medium-sized effect, implying the highest
predictive power of all included exogenous variables.
5 Discussion and Conclusion
Based on our results, hypotheses H1 to H5 can be confirmed, whereas H6 and H7
cannot be confirmed (cf. Table 5). The results for H6 and H7 are very surprising,
Table 5. Summary of the Results
Hypothesis Result
H1:
Internet Users Information Privacy Concerns (IUIPC) have a negative effect
on Trusting Beliefs (TB)
3
H2:
Internet Users Information Privacy Concerns (IUIPC) have a positive effect
on Risk Beliefs (RB)
3
H3: Trusting Beliefs (TB) have a negative effect on Risk Beliefs (RB) 3
H4:
Internet Users Information Privacy Concerns (IUIPC) have a positive effect
on the trusting beliefs in JonDonym (TBJD)
3
H5:
Trusting beliefs in JonDonym (TB
JD
) have a positive effect on the actualuse
behavior of JonDonym (USE)
3
H6:
Trusting beliefs (TB) have a negative effect on actual use behavior of
JonDonym (USE)
7
H7:
Risk beliefs (RB) have a positive effect on actual use behavior of JonDonym
(USE)
7
considering that they are in contrast to the rationale explained in Section 3.1 and
the results from previous literature [
6
]. However, it must be said that it is possible
that the relatively small sample size of 141 leads to a statistical non-significance
when effect sizes are rather small. Therefore, we cannot rule out that the effects
of risk beliefs and trusting beliefs on use would be significant with a larger sample
size. Thus, only the degree of trust in the PET (JonDonym) has a significant and
large effect on the use behavior. This result shows that it is crucial for a PET
provider to establish a trustful reputation to get used. The trusting beliefs in the
PET itself are positively influenced by the users’ information privacy concerns.
Thus, the results imply that users with a higher level of privacy concerns rather
tend to trust a PET. The limitations of the study primarily concern the sample
composition and size. First, a larger sample would have been beneficial. However,
in general, a sample of 141 participants is acceptable for our kind of statistical
analysis [
16
] and active users of a PET are hard to find for a relatively long
online questionnaire. This is especially the case, if they do not have any financial
rewards as in our study. Second, the combination of the results of the German and
the English questionnaire can be a potential source for errors. Participants might
have understood the questionnaire in German differently than the participants
who filled out the English version. We argue that we achieved equivalence with
regard to the meaning through conducting a thorough translation process, and
therefore limiting this potential source of error to the largest extent possible. In
addition, combining the data was necessary from a pragmatic point of view to
get a sample size as large as possible for the statistical analysis.
Further work is required to investigate the specific determinants of use de-
cisions for or against PETs and break down the interrelationships between the
associated antecedents. In particular, it would be interesting to investigate the
relationship between trusting beliefs in online companies and trust in the PET
itself. A theoretical underlying is required to include this relationship in our
structural equation model.
In this paper, we contributed to the literature on privacy-enhancing technolo-
gies and users’ privacy by assessing the specific relationships between information
privacy concerns, trusting beliefs in online firms and a privacy-enhancing tech-
nology (in our case JonDonym), risk beliefs associated with online firms data
processing and the actual use behavior of JonDonym. By adapting and extend-
ing the IUIPC model by Malhotra et al.[
6
], we could show that several of the
assumptions for regular online services do not hold for PETs.
Acknowledgments
This research was partly funded by the German Federal Ministry of Education
and Research (BMBF) with grant number: 16KIS0371. In addition, we thank
Rolf Wendolski (JonDos GmbH) for his help during the data collection process.
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A Questionnaire
The following items are measured with a seven-point Likert scale, ranging from
”strongly disagree” to ”strongly agree”.
Collection (COLL)
1.
It usually bothers me when online com-
panies ask me for personal informa-
tion.
2.
When online companies ask me for per-
sonal information, I sometimes think
twice before providing it.
3.
It bothers me to give personal infor-
mation to so many online companies.
4.
Im concerned that online companies
are collecting too much personal infor-
mation about me.
Awareness (AWA)
1.
Companies seeking information online
should disclose the way the data are
collected, processed, and used.
2.
A good consumer online privacy policy
should have a clear and conspicuous
disclosure.
3.
It is very important to me that I am
aware and knowledgeable about how
my personal information will be used.
Control (CONTROL)
1.
Consumer online privacy is really a
matter of consumers right to exercise
control and autonomy over decisions
about how their information is col-
lected, used, and shared.
2.
Consumer control of personal informa-
tion lies at the heart of consumer pri-
vacy.
3.
I believe that online privacy is invaded
when control is lost or unwillingly re-
duced as a result of a marketing trans-
action.
Trusting Beliefs (TB)
1.
Online companies are trustworthy in
handling information.
2.
Online companies tell the truth and
fulfill promises related to information
provided by me.
3.
I trust that online companies would
keep my best interests in mind when
dealing with information.
4.
Online companies are in general pre-
dictable and consistent regarding the
usage of information.
5.
Online companies are always honest
with customers when it comes to using
the provided information.
Risk Beliefs (RB)
1.
In general, it would be risky to give
information to online companies.
2.
There would be high potential for loss
associated with giving information to
online firms.
3.
There would be too much uncertainty
associated with giving information to
online firms.
4.
Providing online firms with informa-
tion would involve many unexpected
problems.
5.
I would feel safe giving information to
online companies.
Trusting Beliefs in JonDonym
(TBJD)
1. JonDonym ist trustworthy.
2.
JonDonym keeps promises and com-
mitments.
3.
I trust JonDonym because they keep
my best interests in mind.
Use Behavior (USE)
1. Please choose your usage frequency for JonDonym3
Never
Once a month
Several times a month
Once a week
Several times a week
Once a day
Several times a day
Once an hour
Several times an hour
All the time
3The frequency scale is adapted from Rosen et al. [29].
... We conducted a survey among users of the anonymization services JonDonym (141 valid questionnaires [80,85]) and Tor (124 valid questionnaires [83,86]) to investigate how the users' privacy concerns influence their behavioral intention to use the service. ...
... JonDonym Users, IUIPC, Path Estimates and Adjusted R 2 -values of the Structural Model[80] ...
Thesis
Full-text available
In order to address security and privacy problems in practice, it is very important to have a solid elicitation of requirements, before trying to address the problem. In this thesis, specific challenges of the areas of social engineering, security management and privacy enhancing technologies are analyzed: Social Engineering: An overview of existing tools usable for social engineering is provided and defenses against social engineering are analyzed. Serious games are proposed as a more pleasant way to raise employees’ awareness and to train them. Security Management: Specific requirements for small and medium sized energy providers are analyzed and a set of tools to support them in assessing security risks and improving their security is proposed. Larger enterprises are supported by a method to collect security key performance indicators for different subsidiaries and with a risk assessment method for apps on mobile devices. Furthermore, a method to select a secure cloud provider – the currently most popular form of outsourcing – is provided. Privacy Enhancing Technologies: Relevant factors for the users’ adoption of privacy enhancing technologies are identified and economic incentives and hindrances for companies are discussed. Privacy by design is applied to integrate privacy into the use cases e-commerce and internet of things.
... That changed with a series of papers investigating reasons for the (non-)adoption of Tor [20] and JonDonym [17]. Based on the construct of internet users' information privacy concerns [42,43] Harborth and Pape found that trust beliefs in the anonymization service played a huge role for the adoption [18,19]. Further work [21] indicates that the providers' reputation, aka trust in the provider, played also a major role in the users' willingness to pay for or donate to these services. ...
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Users report that they have regretted accidentally sharing personal information on social media. There have been proposals to help protect the privacy of these users, by providing tools which analyze text or images and detect personal information or privacy disclosure with the objective to alert the user of a privacy risk and transform the content. However, these proposals rely on having access to users' data and users have reported that they have privacy concerns about the tools themselves. In this study, we investigate whether these privacy concerns are unique to privacy tools or whether they are comparable to privacy concerns about non-privacy tools that also process personal information. We conduct a user experiment to compare the level of privacy concern towards privacy tools and non-privacy tools for text and image content, qualitatively analyze the reason for those privacy concerns, and evaluate which assurances are perceived to reduce that concern. The results show privacy tools are at a disadvantage: participants have a higher level of privacy concern about being surveilled by the privacy tools, and the same level concern about intrusion and secondary use of their personal information compared to non-privacy tools. In addition, the reasons for these concerns and assurances that are perceived to reduce privacy concern are also similar. We discuss what these results mean for the development of privacy tools that process user content.
... However, Schoentgen and Wilkinson [77] also note that users of digital services face the ethical dilemmas of self-responsibility and choice making and that the best way to drive awareness of ethics is education and data literacy. In particular for privacy enhancing technologies, this is not new, as for Tor 11 and Jondonym 12 , two tools safeguarding against mass surveillance, trust in the technology has been shown to be one of the major drivers [37,38,39,41]. The trust in the technology was driven by online privacy literacy [40] supporting Schoentgen and Wilkinsons' theory. ...
Chapter
Enabling cybersecurity and protecting personal data are crucial challenges in the development and provision of digital service chains. Data and information are the key ingredients in the creation process of new digital services and products. While legal and technical problems are frequently discussed in academia, ethical issues of digital service chains and the commercialization of data are seldom investigated. Thus, based on outcomes of the Horizon2020 PANELFIT project, this work discusses current ethical issues related to cybersecurity. Utilizing expert workshops and encounters as well as a scientific literature review, ethical issues are mapped on individual steps of digital service chains. Not surprisingly, the results demonstrate that ethical challenges cannot be resolved in a general way, but need to be discussed individually and with respect to the ethical principles that are violated in the specific step of the service chain. Nevertheless, our results support practitioners by providing and discussing a list of ethical challenges to enable legally compliant as well as ethically acceptable solutions in the future.
... On human factors of PETs, we have the, e.g., technology acceptance of Tor/JonDonym [21] and anonymous credentials [4]. While these two studies used "perceived anonymity" as a three-item scale, subsequent work by Harborth and Pape used IUIPC as privacy concern scale to evaluate JonDonym [19] and Tor [20]. ...
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