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In this study, we present a new scheme for smart education utilizing the concept of a blockchain for question sharing. A two-phase encryption technique for encrypting question papers (QSPs) is proposed. In the first phase, QSPs are encrypted using a timestamp, and in the second phase, previously encrypted QSPs are encrypted again using a timestamp, salt hash, and hash from the previous QSPs. These encrypted QSPs are stored in a blockchain along with a smart contract that helps the user to unlock the selected QSP. An algorithm is also proposed for selecting a QSP for the exam that randomly picks a QSP. Moreover, a timestamp-based lock is imposed on the scheme so that no one can decrypt the QSP before the allowed time. Security analysis is conducted to demonstrate the feasibility of the proposed scheme against different attacks. Finally, the effectiveness of the proposed scheme is demonstrated through implementation, and the superiority of the proposed scheme over existing schemes is proven through a comparative study based on different features.
J. lnf. Commun. Converg. Eng. 17(3): 174-184, Sep. 2019
Regular paper
15 August 2019,
18 September 2019,
18 September 2019
Corresponding Author
Soo Young Shin (E-mail:, Tel: +82-54-478-7473)
Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.
print ISSN: 2234-8255 online ISSN: 2234-8883
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (
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The Korea Institute of Information and Communication Engineering
BSSSQS: A Blockchain-Based Smart and Secured Scheme for
Question Sharing in the Smart Education System
Anik Islam , Md Fazlul Kader , and Soo Young Shin
Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
In this study, we present a new scheme for smart education utilizing the concept of a blockchain for question sharing. A two-
phase encryption technique for encrypting question papers (QSPs) is proposed. In the first phase, QSPs are encrypted using a
timestamp, and in the second phase, previously encrypted QSPs are encrypted again using a timestamp, salt hash, and hash from
the previous QSPs. These encrypted QSPs are stored in a blockchain along with a smart contract that helps the user to unlock the
selected QSP. An algorithm is also proposed for selecting a QSP for the exam that randomly picks a QSP. Moreover, a
timestamp-based lock is imposed on the scheme so that no one can decrypt the QSP before the allowed time. Security analysis is
conducted to demonstrate the feasibility of the proposed scheme against different attacks. Finally, the effectiveness of the
proposed scheme is demonstrated through implementation, and the superiority of the proposed scheme over existing schemes is
proven through a comparative study based on different features.
Index Terms
Blockchain, Internet of Things, Security, Smart education
Blockchain has brought a revolution in the realm of tech-
nology [1, 2] and has started to draw the interest of the
stakeholders of a wide span of industries including finance,
healthcare, and digital content distribution [3-8]. In blockchain,
when a transaction occurs in the network, the transaction has
to experience validation called a consensus mechanism, a
process where some of the participants reach a mutual agree-
ment in allowing that transaction [9]. Each block contains
the hash of the previous block because of which it is called a
blockchain [10]. In a blockchain, asymmetric cryptography
is adopted to issue transactions [11, 12]. Internet of Things
(IoT) has brought another revolution in the realm of technol-
ogy [13-16]. Recently, IoT has established its mark in the
education sector [17, 18]. Smart campuses, smart class-
rooms, digital content, and campus safety are some of the
results of IoT. However, IoT technology is facing security
risks. Entities in IoT need reliable and tamper-proof protec-
tion from attacks like denial-of-service. [19]. Blockchain can
mitigate this issue with its security infrastructure [20, 21].
Examination is an important part of education [22, 23].
However, there is a threat, named question paper leaking
(QPL), which can cause an unfairness issue during examina-
tions. Nowadays, QPL is a serious issue throughout the
world from university entrance examinations to public exam-
inations, and the situation is worse in developing countries
[24-27]. QPL can lead to some serious outcomes, such as
quality of education being compromised and erosion of ethi-
cal standards [27]. Some more cases have been previously
reported [28-47]. Although these cases [28-47] only cover
the QPL incidents that occurred in 2017, some countries face
this problem almost every year. Therefore, QPL occurs not
only within developing and underdeveloped countries but
BSSSQS: A Blockchain-Based Smart and Secured Scheme for Question Sharing in the Smart Education System
also in developed countries. In QPL incidents, along with the
students teachers and authorities can also be involved.
Therefore, a smart examination system needs to be devel-
oped that can share examination papers securely without the
concern of QPL. Moreover, social engineering, phishing etc.,
can loot anyone’s credentials to access data anytime. There-
fore, examination management systems need more than user
credential and random question selection. Question sharing
(QS) should be performed through a more robust system,
where user credential is less important. In this study, a
blockchain-based smart and secured QS scheme (termed as
BSSSQS) is proposed, a topic that has not been explored yet
to the best of our knowledge. The major contributions of this
study are as follows:
• The proposed scheme can increase the security of ques-
tions and provide seamless QS sharing.
A two-phase encryption technique is proposed to provide
security for question papers (QSPs).
A randomization algorithm is proposed for selecting
QSPs before the exam.
The proposed scheme has been implemented, and the
performance of the system has been analyzed.
The remaining sections of this paper are organized as fol-
lows: a discussion on existing works is presented in Section
II. Section III illustrates the system model of BSSSQS. In
Section IV, different transactions of BSSSQS are discussed
in detail. A security analysis against different attacks is
demonstrated in Section V. Section VI represents a discus-
sion on performance analysis. Finally, Section VII draws the
conclusions from this study.
To digitalize the examination system, different ideas have
been shared previously [48-54]. In [48], three models of web
examination system, such as B/S, C/S, and B/S combined
with C/S, are discussed. Another web-based examination
system was proposed for distant and formal education in
[49]. In [50], an online examination system for PE theory
courses was proposed, where every user uses a username and
password to access the portal. In [51], an online examination
system was proposed where MD5 encryption technique was
exploited for security. An examination management system
based on flat network was demonstrated by [52], which pro-
vides role-based security. In [53], a web-based examination
system was proposed and integrated with existing learning
management systems, whereas an online examination system
based on TCP/IP client-server architecture was proposed in
[54]. These systems mainly focus on system design and
overall management. However, they could not guarantee to
solve the QPL incidents.
We devised a blockchain-based QS scheme to make it
secure and smart, as shown in Fig. 1. Four major entities are
described in the subsections.
A. Question Setter (QUS)
In this entity, actors have to submit questions for the
exam. They have a deadline for submitting questions. They
can modify or delete questions before submitting. However,
once they submit the question, they cannot change the ques-
tions anymore.
B. Question Cloud (QC)
This entity involves initial management of questions. After
getting questions from the QUS, QC stores questions before
sending it to the next entity. In this entity, questions are
modified to prepare QSPs, where a QSP consists of a set of
questions. Then, the QSPs are handed over to the next entity.
This entity consists of seven modules. The functions of each
module are summarized as follows. Question cloud manager
(QCM): manages internal functionality; signature verifier
(SV): verifies signatures of the requester; format question
(FQ): formats and modifies the questions; question pool
(QNP): stores modified questions; question filter (QF): sorts
and makes sets; question queue (QQ): stores questions; and
database (DB): contains information like signature and course.
This entity holds the information of all the connected min-
ions (nodes) to which it sends QSPs. This entity also main-
tains communication with its minions through a blockchain
cloud. It also selects a QSP for the exam. Furthermore, this
Fig. 1.
System model of the proposed BSSSQS
J. lnf. Commun. Converg. Eng. 17(3): 174-184, Sep. 2019
entity performs security mechanisms on QSPs. It comprises
thirteen modules. The functions of each module are summa-
rized here. Question queue (QNQ): stores QSPs temporarily;
BSSSQS master manager (BMM): manages internal func-
tionality; timestamp (TS): converts date to timestamp; ques-
tion set (QS): organizes QSPs based on the course list; salt
engine (SE): generates random data; data encryptor (DE):
encrypts QSPs; encryption factory (EF): encrypts QSPs; hash
generator (HG): generates hash of QSPs; contract generator
(CG): generates a smart contract; database (DB): stores data
of QSPs; guffy bot (GB): monitors internal tasks; question
picker (QP): selects a QSP; and exclusion pool (EP): stores
illegal QSPs.
This entity contains processed QSPs in the blockchain. No
one can access QSPs without experiencing smart contract,
timestamp verification, etc. This entity consists of eight modules.
The functions of each module are as follows: BSSSQS minion
manager (BMNM): manages internal functionality; blockchain
(BC): blockchain-based storage; minion bot (MB): monitors
internal activity; smart contract manager (SCM): handles
authorization requests and decrypts QSPs; database (DB):
contains decrypted QSPs; user panel (UP): provides user
interface and manages tasks; session manager (SM): contains
information related to user activeness and authorization; and
user authentication and authorization manager (UAAM): ver-
ifies user credentials.
In this section, we describe the different types of transac-
tions performed in BSSSQS. The list of important notations
with descriptions are summarized in Table 1.
A. Transactions between QUS and QC
Two types of major transactions—authentication of QUS
and questions handover to QC—take place between QUS
and QC. Every user in QUS has a unique signature, which is
stored in QC. Each user has to prove his identity with proper
credentials to send questions to QC, as shown in Fig. 2. The
proposed scheme assumes that all communication between
QUS and QC is done by employing asymmetric key encryp-
tion. Before sending questions, QUS sends a request to QC
to obtain a public key of QC. QUS sends the request by
sending data
. After getting the request from QUS, QC
generates one-time asymmetric keys (OTAKs) for QUS to
transfer credentials and questions. QC generates a secret key
and a public key
. Let
be the set of OTAKs:
is a large prime number,
is the current timestamp,
is a salt hash, and
is a set of (
) coordinates on the
elliptic curve. When QC finishes generating OTAKs, QC
by sending
. Upon receiving
, QUS encrypts
QT and PW employing
). Following this, QUS
+ 1 by 1 and sends data
to QCM. When QC
, QC first decrypts data utilizing
and checks the validity of the credentials provided from QUS.
If the credential is valid, then QC returns a success token by
. Before sending questions, QUS creates a signa-
ture using QT and PW. Let
be the signature:
QUS first encrypts the question using
and then
encrypts using
. After encryption, QUS sends
. As QC
receives data from QUS, QC decrypts data by employing
)). QC generates a signature employing Eq.
(2) and validates the identity of the person by decrypting the
question with the sender’s signature.
B. Transactions between QC and master
Here, transactions are divided into two main categories
shown in Fig. 3: 1) processing questions within different
modules of QC, and 2) sending QSPs from QQ to BSSSQS-
for further processing. After the deadline of question
submission, FQ formats and modifies the questions to pre-
Table 1.
Notations and their descriptions
Notation Description
Nonce, Prime number, Question
Timestamp, Salt hash, Questionnaire token
, sm
Token, Encrypted QSP, Smart contract
(·) One way key generation function
(.) Encryption and Decryption function
Fig. 2.
Transactions between QUS and QC
BSSSQS: A Blockchain-Based Smart and Secured Scheme for Question Sharing in the Smart Education System
pare QSPs. The QSPs are then sent to QNP, where the ques-
tions are stored temporarily, and after obtaining proper
instructions from QCM, QSPs are sent to QF. QF selects
some QSPs based on certain criteria and forwards these
selected QSPs to QQ before sending to BSSSQS
. When
the collection is finished, QQ sends QSPs to BSSSQS
C. Transactions between Master and Minion
This segment covers transactions between BSSSQS
, as shown in Fig. 4. The primary tasks of
are summarized as follows: (1) to encrypt
QSPs and send these encrypted QSPs to BSSSQS
(2) to select a QSP for the exam and send permission notifi-
cation to BSSSQS
for accessing the selected QSP.
plays a very significant role in providing secu-
rity to QSPs. Initially, questions are stored in QNQ. After
getting QSPs from QNQ, BMM picks the timestamp
sending a request to TS. In the subsequent stage, BMM pulls
the course list from DB. Next, BMM sends QSPs to QS with
and the course list. Then, QSPs experience two-phase
encryption as follows:
1) First-phase Encryption
The first phase of encryption is managed by QS. First, QS
requests SE for generating a salt hash
. After getting
from SE, QS stores it for the next phase of encryption. Sec-
ond, QS sends QSPs to DE with
. DE then encrypts QSPs
. Let
be the
number of QSPs. Therefore,
ber of QSPs that experience the first phase of encryption is
written by
Finally, QS sends encrypted QSPs to EF with
2) Second-phase Encryption
The second phase of encryption happens in EF. EF gener-
Fig. 3.
Transactions between QC and BSSSQS
Fig. 4.
Transactions between BSSSQS and BSSSQS .
J. lnf. Commun. Converg. Eng. 17(3): 174-184, Sep. 2019
ates a default genesis block (i.e., first block) with random
text and encrypts it with
. After creating the genesis block,
EF encrypts QSPs and converts them into blocks. Each block
contains a header and data. The header carries the previous
block hash, timestamp, last access time, block creation time,
and nonce. Every time EF encrypts a QSP, it sends that
encrypted QSP to HG. HG then generates a hash from that
encrypted QSP to participate in the next QSP encryption.
Therefore, the encrypted QSP that experiences the second
phase of encryption can be written as
. The hash of the
QSP is generated
). Then, HG stores the generated hash
in DB for QSP selection. Next, EF commands CG to gener-
ate a smart contract including the information of
, and
. The smart contract contains hashes of QSPs, timestamp,
and salt hash. When smart contract generation is completed,
CG encrypts the smart contract with a timestamp
and a
random salt hash
. Let
be the encrypted smart contract.
After the encryption, CG stores the key in DB. At the time
of the exam, BSSSQS
sends the key along with a
selected question hash. After getting the encrypted smart
contract from CG, EF sends blocks and the smart contract to
GB. As GB gets the blocks and contract, it initiates the pro-
cess of sending these resources to BSSSQS
. At first,
GB pulls the existing minion list from DB. When GB get all
lists, it begins sending blocks and the contract to BSSSQS
through the blockchain cloud. When a QSP has to be
selected for an exam, GB sends an instruction to QP for ini-
tiating the process of selecting a QSP for the exam along
and notifying the minions about that QSP. Before
initiating random engine for picking out a QSP, QP pulls the
hash of QSPs from DB. Meanwhile, it also requests EP to
send the hashes of the excluded QSPs. When QP gathers all
the required information, it starts the process of selecting a
QSP as follows. First, QP removes the excluded QSPs from
the set of QSPs. Therefore, the set of filtered QSPs is
written by
where is the set of all QSPs and is the set of
excluded QSPs. Second, QP takes a collection of 10 large
prime numbers which is represented by
Next, it converts the current date and time into a timestamp
τ. To select any two prime numbers from
, QP takes the last
and second last digit
to select prime num-
, respectively. The selected QSP to take the
exam is represented by
is the total number of filtered QSPs and
As QP selects a QSP, it notifies all BSSSQS
about the
selection through the blockchain cloud.
D. Transactions in BSSSQS
This section covers the transactions between different
modules of BSSSQS
, as shown in Fig. 5. Note that U in
Fig. 5 represents a user in the system. The transactions are
categorized into the following three types: (1) storing and
maintaining QSP blocks in blockchain, (2) updating the
smart contract, and (3) alerting authority about the permis-
sion to access QSPs. After getting blocks and the smart con-
tract, BMNM sends blocks to BC and smart contract to SCM
for the selected exam. When BMNM gets a QSP selection
notification from BSSSQS
, BMNM passes this notifica-
tion to UP and UP alerts users about the access. When a user
tries to enter UP, he has to experience a validation process.
UAAM sends a request to DB to send information regarding
the requested signature. If the user is valid, DB returns user
information, otherwise, it reruns empty data. When UAAM
gets validation from DB, it stores a token in SM for main-
taining the user session. Every minion manages its users by
itself. After that, UAAM notifies UP about the response. As
users get a notification about the QSP and key for decrypting
, they request for QSP through UP. UP requests SCM to
start the decryption process. Before going further, SCM
sends a command to MB to check whether QSP is unlocked
for access. MB affirms authorization with BSSSQS
With proper authorization, SCM transfers the request to BC.
BC performs a final authorization check with BSSSQS
through MB. If BC gets an unauthorized request with a QSP,
it changes access time and nonce and mines the chain again.
It changes the signature of all the QSPs, and no one can get
its key hash. Whenever BC gets an affirmative result, it
sends the QSP to SCM for decrypting. First, SM decrypts
to the selected QSP. Let
be the decrypted smart con-
)}. After getting
decryption begins. Let
be a selected encrypted QSP. By
in Eq. (4), the first phase of decryption is writ-
ten by
goes through the second phase of decryption
which is written by
is the QSP which experiences the second phase of
BSSSQS: A Blockchain-Based Smart and Secured Scheme for Question Sharing in the Smart Education System
decryption. After decryption, SC stores the QSP to DB and
sends a notification to UP about the outcome. Finally, users
can retrieve QSP from DB to take an exam.
In this section, we propose different propositions related to
security against different attacks with proof.
Proposition 1.
The secret key of QC is well protected
from the adversary.
QC’s secret key is generated using Eq. (1). Sup-
pose, adversary
wants to steal the secret key of QC. The
only way to get the secret key of QC is to guess the private
key, to the best of our knowledge, as QC never shares its pri-
vate key with anyone. However, in asymmetric encryption,
suppose the secret key is 256 bits long. To guess the correct
secret key,
needs to guess the sequence of 256. For 256
bits, there are 2
possible sequences, and among them,
only one can be the QC’s secret key. The probability of
guessing the secret key is 1/2
which is practically
not feasible. Moreover, if
wants to guess the properties of
the secret key individually,
has to face the probability of
randomness in each property which is also practically not
feasible. Furthermore, OTAK is temporary. When questions
are transferred successfully, OTAK, which is generated for
particular QUS, is removed from QC. Therefore, the secret
key may become obsolete while
is still trying to guess the
secret key. Thus, QC’s secret key is well protected from the
Proposition 2.
Communication between QUS and QC is
secure even in the presence of an eavesdropper.
The motive behind the communication between
QUS and QC is to transfer questions. To send questions,
QUS requires QC’s public key to create a digital signature
using Eq. (2) and encrypt questions employing
However, when QUS requests for the OTAK, QC generates
OTAK utilizing Eq. (1). When QUS gets
, first, QUS val-
idates its identity by transmitting
, which is
encrypted using
, to QC. Second, it generates a digital
signature by applying Eq. (2). Finally, QUS encrypts ques-
tions using
and sends the questions back to QC signed
with its signature. Suppose, there exists an eavesdropper
between QC and QUS.
wants to steal the creden-
tials of QUS along with questions and also wants to send
false data to QC.
catches data between QUS and QC and
wants to extract
data, as shown in Fig. 2. To
extract data,
requires QC’s private key, which is not avail-
able to anyone except QC. Moreover, there is no feasible
solution to extract the private key from public key by reverse
engineering or guessing, as discussed in Proposition 1. How-
wants to send false data encrypted by
to QC.
When QUS send questions to QC, QUS signs the question
with its signature. From the signature, QC verifies the actual
source of the data. As
needs the signature of QUS,
not send false data until it obtains QUS’s signature and not
only is QUS’s signature not only publicly available but also
QUS never shares its signature with other people apart from
Fig. 5.
Transactions in BSSSQS
J. lnf. Commun. Converg. Eng. 17(3): 174-184, Sep. 2019
QC in an encrypted form. As a result,
cannot achieve any
of the aforementioned objectives.
Proposition 3.
QSP selection in BSSSQS
is totally
random and is free from compromised QSPs.
Before the exam, BSSSQS
selects a QSP and
sends that QSP reference to BSSSQS
. This process is
completely random. Before selecting a QSP, BSSSQS
selects a set of 10 prime numbers. Each prime number is
selected following uniform distribution. Let,
be the set of
prime numbers and
be the set of already selected prime
numbers. Therefore, the probability of selecting prime num-
bers is
). Finally, QSP is selected by employing Eq.
(7) which gives a random QSP number. BSSSQS
is a
well-protected scheme. By any chance, if any QSP becomes
compromised, BSSSQS
notifies BSSSQS
that question. BSSSQS
excludes that compromised QSP
from the selection process by employing Eq. (6) to remove
compromised QSPs.
Proposition 4.
QSPs and smart contract are secure from
physical attacks by both insiders and outsiders.
Physical attacks involves exploiting the weakest
point by the attacker to breach the security system. However,
attackers may not always come from outside. Sometimes, a
person from the inside may also harm the system. As we dis-
cussed in the Introduction, sometimes a teacher or authority
may leak the question, so it is very important to provide pro-
tection from attacks is caused by both outsiders and insiders.
BSSSQS imposes a timelock on the QSPs and the smart con-
tract. If anyone tries to access both of them before the
allowed time, the system notifies not only BSSSQS
. Suppose, an attacker
from inside
wants to steal QSPs.
disables the connection of BSSSQS-
and tries to copy QSPs from a disk. QSPs access per-
mission is locked, which can be unlocked by the permission
. However,
somehow bypasses the
access protection and starts to copy. An internal monitoring
system monitors this activity and changes the QSP auditing
time, which changes the hash of QSPs and breaks the chain
of the block. When BSSSQS
comes online, BSSSQS
notifies BSSSQS
, and BSSSQS
excludes the
from taking the exam. However, after copying
the QSPs,
needs a private key to unlock both QSPs and
the smart contract, which are encrypted by employing Eq.
(4) and Eq. (5), respectively. In Proposition 1, we discussed
that it is not feasible to guess a key. Therefore, copying the
QSPs will not help
. This outcome is the same for outsiders
too. In this way, QSP and smart contracts are secure from
physical attacks.
In this section, we discuss the experimental results and
compare the proposed BSSSQS with existing schemes based
on different features to demonstrate the feasibility of BSSSQS.
Fig. 6.
Experimental result performed in BSSSQS: (a) time to transfer questions (TTQ) from QUS to QC, (b) processing time for performing security actions for
different size of QSPs, (c) time for creating block in blockchain, (d) size of the block after adding QSPs, and (e) time to select QSP before the exam for different
number of QSPs
BSSSQS: A Blockchain-Based Smart and Secured Scheme for Question Sharing in the Smart Education System
A. Experimental Results
Three computers were considered for the experiment.
Intel(R) Core(TM) i5-4670 CPU @ 3.40GHz was considered
as QUS with 16 GB. Microsoft Windows 10 Professional 64-
bit was used as an operating system (OS) in QUS. Intel(R)
Xeon(R) Processor E5-2697A V4 @ 2.60 GHz was consid-
ered as QC and BSSSQS
with 32 GB. CentOS 7.5 was
utilized as an OS in QC and BSSSQS
. Intel(R)
Core(TM) i5-4670 CPU @ 3.40GHz was considered as
with 32 GB. Ubuntu 18.04.1 LTS was utilized
as an OS in BSSSQS
. RSA was considered for asym-
metric encryption, and Twofish was considered for symmet-
ric encryption. The middleware in QUS was built using
Node.js, the middleware in QC was built using PHP, and the
middleware in BSSSQS
and in BSSSQS
was built
using Python. The blockchain network was built and main-
tained using Python. As the proposed BSSSQS is a private
network and blocks were created in BSSSQS
, no con-
sensus mechanism was considered during the experiment.
Fig. 6 represents the result of the experiments performed in
BSSSQS. In Fig. 6(a), time to transfer questions (TTQ) from
QUS to QC is demonstrated for different question sizes.
Requesting public key and validation of user identity is also
included in TTQ. With the increasing size of questions, TTQ
also increases. This is because more time is required for
encrypting and transferring larger questions over the net-
work. Fig. 6(b) illustrates the processing time for performing
security mechanisms in BSSSQS
The processing time increased with increasing QSP size for
phase-1 and phase-2 encryption in BSSSQS
and phase-1
and phase- 2 decryption in BSSSQS
. The computation
power in BSSSQS
is much higher than that of BSSSQS-
. Thus, the processing time in BSSSQS
is lesser
than that in BSSSQS
. Eq. (3) and Eq. (4) were utilized
for calculating the processing time while performing phase-1
and phase-2 encryption in BSSSQS
, respectively. Eq.
(8) and Eq. (9) were used for calculating the processing time
while performing phase-1 and phase-2 decryption in
, respectively. Phase-1 encryption requires less
time than phase-2. This is because the key is generated from
the previous block’s hash, and the size of the key in phase-2
increases with the increase of block. Phase-1 decryption
takes more time than phase-2 decryption. To decrypt in
phase-2, the hash of the previous block is required and a
combination of the previous block’s hash increases the key
size. Fig. 6(c) depicts the block creation for different QSP
sizes. With the increase in the size of QSP, block creation
time also increases. The block contains QSP, timestamp,
nonce, and the previous block’s hash. While creating blocks,
data were encrypted. After preparing the aforementioned
attributes, the block is created and a hash is generated that
works as the identity of the block. The higher the QSP size,
the more time is required for encrypting data and generating
the hash. Thus, the block time increases. Fig. 6(d) demon-
strates the change in the block size for different QSP sizes.
With increasing size of QSP, block size also increases. Fig.
6(e) portrays the change in time for selecting the QSP for the
different number of QSPs. Eq. (6) and Eq. (7) were used
during calculating time for selecting the QSP. When the
number of QSPs is increased, QSP selection time also
increases because the more QSPs in the list, the more time is
required to filter compromised QSPs. Overall, the increase in
QSP selection time is very small.
B. Performance Comparison
A comparative study between BSSSQS and existing models
([48]-[54]) was performed, as shown in Table 2, where (√)
means supported and (×) means not supported, by considering
the following features.
Secure login: This feature covers the security in the
login process like password encryption, random pass-
word, etc. BSSSQS along with all of the existing models
(([48]-[54]) support secure login.
• QSP generation: This feature generates a QSP randomly
from a list of questions. BSSSQS randomly generates
QSPs from the provided questions and among the exist-
ing systems. Chang [48], Lu [51], Zhai [52], Kaya [49],
and Younis [54] supported this.
QSP encryption: This feature encrypts the QSP to prevent
unauthorized access. Only BSSSQS performs encryption
in QSPs.
• QSP selection: This feature supports the random selec-
tion of a QSP. BSSSQS randomly selects a QSP, and
among the existing systems, Henke [53], Kaya [49], and
Younis [54] support this feature.
• Timestamp lock: This feature helps impose a restriction
of time on QSPs so that no one can access QSPs before
the allowed time. Only BSSSQS imposes a timestamp
lock on the QSPs.
Table 2.
Performance comparison.
Yang [50]
Chang [48]
Lu [51]
Zhai [52]
Henke [53]
Kaya [49]
Younis [54]
Secure login
QSP generation
QSP encryption
QSP selection
Timestamp lock
J. lnf. Commun. Converg. Eng. 17(3): 174-184, Sep. 2019
In this study, we proposed a secured QS scheme exploiting
the security mechanism of blockchain. In this scheme, QSP
experiences two-phase encryption to prevent unethical
access before the exam. Moreover, a restriction of time is
issued in the proposed scheme so that every minion has to
wait for system permission to initiate the decryption process
of QSP. Furthermore, QSP is selected by master employing
the proposed randomize algorithm. A combination of these
features can provide a secured QS system. We analyzed
BSSSQS’s security by proposing different propositions with
proofs. We compared the performance of BSSSQS with
other existing education management schemes. Based on the
theoretical comparison, it can be demonstrated that BSSSQS
is more secure than other models. We implemented BSSSQS
and performed experiments on the implementation to show
the effectiveness of BSSSQS. Therefore, we can conclude
that BSSSQS can be a promising approach for providing
proper security to mitigate the QPL problem in the future
smart education system.
This work was supported by Priority Research Centers
Program through the National Research Foundation of Korea
(NRF) funded by the Ministry of Education, Science and
Technology (2018R1A6A1A03024003).
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J. lnf. Commun. Converg. Eng. 17(3): 174-184, Sep. 2019
Anik Islam
was born in 1992. He received his B.Sc. in software engineering and M.Sc. in computer science from American
International University-Bangladesh (AIUB), Dhaka, Bangladesh, in 2014 and 2017, respectively. He is currently working
toward his PhD degree with the WENS Laboratory, Kumoh National Institute of Technology, Gumi, South Korea. He has
more than five years of experience of working in the software development field. He has participated in various software
competitions with good achievements. His major research interests include blockchain, Internet of Things, unmanned
aerial vehicles, social Internet of Things, mobile edge computing, web of things, semantic web, wireless network, and
distributed systems.
Md Fazlul Kader
received the B.Sc. and M.Sc. degrees in computer science and engineering from the Chittagong University of Engineering
and Technology, Chittagong, Bangladesh, in November 2005 and January 2014, respectively. He was awarded the Ph.D.
degree from the Kumoh National Institute of Technology, Gumi, South Korea, in February 2018. Since 2007, he has been
a faculty member with the Department of Electrical and Electronic Engineering, University of Chittagong, Chittagong,
Bangladesh, where he is currently an Associate Professor. He has co-authored more than 45 technical papers in
international journals and conference proceedings. He is an Associate Editor of the IEEE Access. Moreover, he regularly
serves as a reviewer and TPC member in many reputed journals and conferences. His major research interests include
5G, cognitive radio networks, cooperative communications, MIMO, computer networks, NOMA, spatial modulation,
blockchain, internet of things, etc.
Soo Young Shin
received his Ph.D. degrees in electrical engineering and computer science from Seoul National University on 2006. He
was with WiMAX Design Lab, Samsung Electronics, Suwon, South Korea from 2007 to 2010. He joined as full-time
professor to School of Electronics, Kumoh National Institute of Technology, Gumi, South Korea. He is currently an
Associate Professor. He was a post Doc. researcher at University of Washington, Seattle, WA, USA from 2006 to 2007. In
addition, he was a visiting scholar to University of the British Columbia at 2017. His research interests include wireless
communications, next generation mobile wireless broadband networks, signal processing, Internet of things, etc.
... An initiative to share exam questions and protect them from being leaked is proposed and analyzed by Islam et al. [22]. Question papers are encrypted with a timestamp and trustfully stored in the blockchain with a smart contract that controls when they can be accessed. ...
... Arndt and Guercio [13] EER REVIEW 10 of 25 [16]   Dai et al. [18] Daraghmi et al. [19]   Ghazali and Saleh [20] Guo et al. [21]  Islam et al. [22]  Jeong and Choi [23]  KARATAŞ [24]  Lam and Dongol [25]   Li and Han [26]   Li et al. [27]   Lizcano et al. [28]   Novikov et al. [30] Ocheja et al. [31]   Palma et al. [32]  Prinz et al. [34]  Rooksby and Dimitrov [36]  Saleh et al. [37] Sun et al. [38] Turkanović et al. [39]  Ubaka-Okoye et al. [41] Appl. Sci. ...
... ions and applications category.   Dai et al. [18] Daraghmi et al. [19]   Ghazali and Saleh [20] Guo et al. [21]  Islam et al. [22]  Jeong and Choi [23]  KARATAŞ [24]  Lam and Dongol [25]   Li and Han [26]   Li et al. [27]   Lizcano et al. [28]   Novikov et al. [30] Ocheja et al. [31]   Palma et al. [32]  Prinz et al. [34]  Rooksby and Dimitrov [36]  Saleh et al. [37] Sun et al. [38] Turkanović et al. [39]  Ubaka-Okoye et al. [41] Vargas and Soriano [42]   Wahab et al. [43] Wanotayapitak et al. [45] Williams [46] ...
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... This paper presents a new smart education scheme, by using the blockchain concept to share questions [38]. To shuffle questions, they used a two-phase encryption technique using timestamp, salt hashing, smart contract and a technique of random algorithms [38]. ...
... This paper presents a new smart education scheme, by using the blockchain concept to share questions [38]. To shuffle questions, they used a two-phase encryption technique using timestamp, salt hashing, smart contract and a technique of random algorithms [38]. ...
... In this section we will discuss the main technologies like Blockchain, Hashing, OTAC, Digital Signature and others. Blockchain is a data storing system that is distributed and replicated among the network nodes that participate [38]. ...
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... The data used was of temperature records which were sent as transactions between sensors and local building management systems. The study in [37] developed a blockchain based smart and secured scheme for question sharing in smart education system (BSSQ) using a twophase encryption technique for the encryption of question papers. At the initial stage, the question papers are encrypted using timestamp and in the second phase, the previous QSPs are further encrypted using timestamp, salt hash, and previous hashes. ...
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... Many researchers are working to implement blockchain for storing the data securely both in academy and industry [9][10][11][12] . Blockchain can be a useful answer for different kinds of security threats [13] . In this paper, a blockchain-enabled MEC server assisted CO 2 emission reduction scheme has been proposed using IoT. ...
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The recent increase in post-examination malpractice at the grade twelve (12) level at marking centers has sparked widespread concern among education stakeholders. The management process at a marking center involves scoring, transfer of grades from hardcopy scripts onto the softcopy system provided by the Examination Council of Zambia and conducted by examiners from various subject panels. However, pockets of post-examination malpractice have been recorded, which include the change of grades initially assigned to a particular candidate’s script at the data entry point. The research is based on finding measures to combat examination malpractice through the use of Blockchain Technology, to stop the post modification of entered results on the Electronic Results Management System of grade 12 results. A baseline study was conducted using purposive sampling of different respondents, i.e., Chief-Examiners, Team Leaders, and Markers, and a prototype using Blockhcain Technology was developed to address this need by the current ERMS. The technology would employ the SHA-256 hashing technique. The final hash document would be shared from a marking center to a central point at the examination council for the verification process before publication of the results. The proposed system showed improved service delivery at marking centers through making it impossible to experience grade modification.KeywordsBlockchain technologyMarking centerSHA-256Examination malpracticeHashingManagement
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Storing data in real time along with keeping it secure is the biggest challenge in industry today. Many land issues arise because there is no database which is protected for tracking the real-time changes in data. Also, land records currently are registered in paper format. This kind of data is thus vulnerable to any changes and maybe destroyed by natural or man-made disasters. The emerging blockchain technology is a boon to store any information in real time and is immune to any changes. In this paper, we propose a solution in the form of distributed app (DApp) which uses the idea of blockchain as distributed database, smart contracts using ethereum platform and Polyline API from Google to mark the land boundaries. Smart contracts allow the performance of credible transactions by using sophisticated cryptography and without interference from third parties. These transactions are traceable and irreversible. Proponents of smart contracts claim that many kinds of contractual clauses may be made partially or fully self-enforcing. In this case, along with the self-verifiable clauses, involving banking parties can perform additional monetary checking. A user can sell or transfer a property that he owns or may buy a new land plot open for sale in desired geographical area. This solution allows maintaining land records easily and in real time without having a single point of failure for the database system. Removal of third-party interventions such as brokers from the process of land title transfer between old and new owners makes the process more transparent and cheaper.
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Privacy, facilitated by a confluence of cryptography and decentralization, is one of the primary motivations for the adoption of cryptocurrencies like Bitcoin. Alas, Bitcoins privacy promise has proven illusory, and despite growing interest in privacy-centric blockchains, most blockchain users remain susceptible to privacy attacks that exploit network-layer information and access patterns that leak as users interact with blockchains. Understanding if and how blockchain-based applications can provide strong privacy guarantees is a matter of increasing urgency. Many researchers advocate using anonymous communications networks, such as Tor, to ensure access privacy. We challenge this approach, showing the need for mechanisms through which non-anonymous users can (i) publish transactions that cannot be linked to their network addresses or to their other transactions, and (ii) fetch details of specific transactions without revealing which transactions they seek. We hope this article inspires blockchain researchers to think beyond Tor and tackle these important access privacy problems head-on.
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Blockchain-enabled e-voting (BEV) could reduce voter fraud and increase voter access. Eligible voters cast a ballot anonymously using a computer or smartphone. BEV uses an encrypted key and tamper-proof personal IDs. This article highlights some BEV implementations and the approach’s potential benefits and challenges.
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The IoT is pervading our daily activities and lives with devices scattered all over our cities, transport systems, buildings, homes and bodies. This invasion of devices with sensors and communication capabilities brings big concerns, mainly about the privacy and confidentiality of the collected information. These concerns hinder the wide adoption of the IoT. To overcome them, in this work, we present an Blockchain- based architecture for IoT access authorizations. Following the IoT tendency requirements, our architecture is user transparent, user friendly, fully decentralized, scalable, fault tolerant and compatible with a wide range of today’s access control models used in the IoT. Finally, our architecture also has a secure way to establish relationships between users, devices and group of both, allowing the assignment of attributes for these relationships and their use in the access control authorization.
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Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the cryptocurrencies. Hitherto, it is used for the decentralization of markets more generally, not exclusively for the decentralization of money and payments. The decentralized transaction ledger of blockchain could be employed to register, confirm, and send all kinds of contracts to other parties in the network. In this paper, we thoroughly review state-of-the-art blockchain-related applications emerged in the literature. A number of published works were carefully included based on their contributions to the blockchain's body of knowledge. Several remarks are explored and discussed in the last section of the paper.
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Data assurance and resilience are crucial security issues in cloud-based IoT applications. With the widespread adoption of drones in IoT scenarios such as warfare, agriculture and delivery, effective solutions to protect data integrity and communications between drones and the control system have been in urgent demand to prevent potential vulnerabilities that may cause heavy losses. To secure drone communication during data collection and transmission, as well as preserve the integrity of collected data, we propose a distributed solution by utilizing blockchain technology along with the traditional cloud server. Instead of registering the drone itself to the blockchain, we anchor the hashed data records collected from drones to the blockchain network and generate a blockchain receipt for each data record stored in the cloud, reducing the burden of moving drones with the limit of battery and process capability while gaining enhanced security guarantee of the data. This paper presents the idea of securing drone data collection and communication in combination with a public blockchain for provisioning data integrity and cloud auditing. The evaluation shows that our system is a reliable and distributed system for drone data assurance and resilience with acceptable overhead and scalability for a large number of drones.
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Since its inception, the blockchain technology has shown promising application prospects. From the initial cryptocurrency to the current smart contract, blockchain has been applied to many fields. Although there are some studies on the security and privacy issues of blockchain, there lacks a systematic examination on the security of blockchain systems. In this paper, we conduct a systematic study on the security threats to blockchain and survey the corresponding real attacks by examining popular blockchain systems. We also review the security enhancement solutions for blockchain, which could be used in the development of various blockchain systems, and suggest some future directions to stir research efforts into this area.
Conference Paper
Unmanned aerial vehicle (UAV) is an emerging technology that becomes popular not only in military operation but also in civil applications. Internet of things (IoT) is another popular technology which brings automation in our daily life. Like other areas, IoT also exposes its potential in healthcare. Using IoT sensors, it becomes easy to monitor the health of a user remotely. UAV consolidated with mobile edge computing (MEC) can provide real-time services in outdoor health monitoring. However, communication among them surrounds with cyber threats and data integrity issue. Blockchain is a data structure in which data are shared among peers. In this paper, a blockchain based secure outdoor health monitoring scheme using UAV is proposed for a smart city. In the proposed scheme, health data (HD) are accumulated from users wearable sensors and these HD are transmitted to the nearest MEC server via UAV. Prior to transmitting to MEC, HD experience encryption in order to provide protection against cyber threats. Moreover, after arriving at MEC, HD are diagnosed and if any abnormalities are found in the user’s health, MEC server notifies the user and the nearest hospitals. When the processing is completed, HD are stored in blockchain with the consent of validators. Finally, simulation results and experimental set up are discussed in order to manifest the feasibility of the proposed scheme.
Conference Paper
Non-orthogonal multiple access (NOMA) with successive interference cancellation receiver is considered as one of the most potent multiple access techniques to be adopted in future wireless communication networks. Data security in the NOMA transmission scheme is on much attention drawing issue. Blockchain is a distributed peer-to-peer network enables a way of protecting information from unauthorized access, tempering etc. By utilizing encryption techniques of blockchain, a secured data communication scheme using blockchain in NOMA is proposed in this paper. A two-phase encryption technique with key generation using different parameter is proposed. In the first-phase data is encrypted by imposing users’ public key and in the second phase, a private key of the base station (BS) is engaged for encryption. Finally, the superiority of the proposed scheme over existing scheme is proven through a comparative study based on the different features.
Blockchain and the Internet of Things (IoT) are key technologies that will have a huge impact in the next 10 years for companies in the industrial market. This article describes how these two technologies will improve efficiencies, provide new business opportunities, address regulatory requirements, and improve transparency and visibility.