IRJET-Implementation of Threshold based Cryptographic Technique over Cloud Computing for Data and Key Storage Security

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 06 | June -2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1015
IMPLEMENTATION OF THRESHOLD BASED CRYPTOGRAPHIC
TECHNIQUE OVER CLOUD COMPUTING FOR DATA AND KEY STORAGE
SECURITY
M/S. Paurnima Ghugarkar 1, Prof.H.B.Jadhav2
1Student, Dept. of Computer Engineering, VACOE, Ahmednagar, Maharashtra, India
2Professor, Dept. of Computer Engineering, VACOE, Ahmednagar, Maharashtra, India
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Abstract - The Cloud domain is increasinglyrapidlyand being
used to store and process big data. Due to the volume and
complexity, outsourcing ciphertexts to a cloud is deemed to be
one of the most effective approaches for big data storage and
access. However, checking and verifying the access legitimacy
or reality of a user and securely updating a ciphertext in the
cloud based on a new access policy designated by the data
owner are two major critical challenges to make cloud-based
big data storage effective and practical. Traditional
approaches either completely ignore the issue of access policy
update or delegate the update to a third party authority; but
in practice, access policy update is important for enhancing
security and dealing with the dynamism caused by user join
and leave activities. In this system, counter against cheating
behaviors of the cloud and also enables (i) the data ownerand
eligible users to effectively verify the legitimacy of a user for
accessing the data, and (ii) a user to validate the information
provided by other users for correct plaintext recovery. The
implementation results depict that this system can prevent
CSP from any data attack and safe eligibleusersfromcheating
and resist various attacks such as the collusion attack.
Key Words: Client/server, Distributed applications,
Distributed file system, Distributed databases, etc.
1. INTRODUCTION
In computing, Data Security is a specialized data
compression technique for eliminating unauthorized copies
of repeating data. Related and somewhatsynonymousterms
are intelligent (data) compressionandsingle-instance(data)
storage. This technique isusedtoimprovestorageutilization
and can also be applied to network data transfers to reduce
the number of bytes that must be sent. In the Security
process, unique chunks of data, or byte patterns, are
identified and stored during a process of analysis. As the
analysis continues, other chunks are compared to thestored
copy and whenever a match occurs, the redundant chunk is
replaced with a small reference that points to the stored
chunk. Given that the same byte pattern may occur dozens,
hundreds, or even thousands of times (the match frequency
is dependent on the chunk size), the amount of data that
must be stored or transferred can be greatly reduced.
A Hybrid Cloud is a combinedformof privatecloudsand
public clouds in which some critical data resides in the
enterprise’s private cloud while other data is stored in and
accessible from a public cloud. Hybrid clouds seek to deliver
the advantages of scalability, reliability, rapid deployment
and potential cost savings of public clouds with the security
and increased control and management of privateclouds.As
cloud computing becomes famous, an increasing amount of
data is being stored in the cloud and used by users with
specified privileges, which define the access rights of the
stored data. The critical challenge of cloud storage or cloud
computing is the managementofthecontinuouslyincreasing
volume of data. Architecture of cloud computing shown in
figure 1. Data Security [3],[4]orSingleInstancingessentially
refers to the elimination of redundant data. In the Security
process, unauthorized data is deleted, leaving only one copy
(single instance) of the data to be stored. However, indexing
of all data is still retained should that data ever be required.
In general, the Data Security eliminates the unauthorized
copies of repeating data. This encryptionrequiresmoretime
and space requirements to encode data. In case of large data
storage, the encryption becomes even more complex and
critical. By using the Data Security inside a hybrid cloud, the
encryption will become simpler.
To avoid this duplication of data and to maintain the
confidentiality in the cloud we using the concept of Hybrid
cloud. It is a combination of public and private cloud. Hybrid
cloud storage combines the advantages of scalability,
reliability, rapid deployment and potential cost savings of
public cloud storage with the security and full control of
private cloud storage.
2. LITERATURE SURVEY
2.1 “Secure and Efficient Data Communication Protocol
for Wireless Body Area Networks”
Wireless Body Area Networks (WBANs) are expected to
play a major role in the field of patient-health monitoring in
the near future, which gains tremendous attention amongst
researchers in recent years. One of the challenges is to
establish a secure communication architecture between
sensors and users, whilst addressing the prevalent security
and privacy concerns. In this system, we propose a
communication architecture for BANs, and design a scheme
to secure the data communications between implanted
/wearable sensors and the data sink/data consumers
(doctors or nurse) by employing Ciphertext-Policy Attribute
Based Encryption (CP ABE) [1] and signature to store the

data in ciphertext format at the data sink, hence ensuring
data security. Our scheme achieves a role-based access
control by employing an access control tree defined by the
attributes of the data. We also design two protocols to
securely retrieve the sensitive data from a BAN and instruct
the sensors in a BAN. We analyze the proposed scheme and
argue that it provides message authenticity and collusion
resistance and is efficient and feasible. We also evaluate its
performance in terms of energy consumption and
communication/computation
Advantages:
1. This system provides message authenticity and collusion
resistance and is efficient and feasible.
2. It achieves a role-based access control by employing an
access control tree defined by the attributes of the data.
Disadvantages:
1. This system does not allow user verifiability based on
access mechanism and therefore chances of unauthorized
data access are more.
2.2 “Security andPrivacyforStorageand Computationin
Cloud Computing”
The Secure Data Sharing in Clouds (SeDaSC)
methodologythatprovides:dataconfidentialityandintegrity,
access control, data sharing (forwarding) without using
compute-intensive re-encryption,insiderthreatsecurity,and
forward and backward access control. The Secure Data
Sharing in Clouds methodology encrypts a file with only a
single encryption key. Two different key shares for each of
the usersare generated, withthe user only getting oneshare.
The possession of a single share of a key allows the SeDaSC
methodology to counter the insider threats andtheotherkey
share is stored by a trusted and believable third party, which
is called the cryptographic server. We implement a working
prototype of the SeDaSC methodology and evaluate its
performance based on the time consumed during various
operations.
2.3. “Expanded Top Ten Big Data Security and Privacy
Challenges”
Securityand privacy issues are magnifiedbythevelocity,
volume, and variety of Big Data, such as large-scale cloud
infrastructures, diversity of data sources and formats,
streaming nature of data acquisition and high volume inter-
cloud migration. There- fore, traditional security
mechanisms,whicharetailoredtosecuringsmall-scale,static
(as opposed to streaming) data, are inadequate. In this
system, wehighlight the toptenBigDatasecurityandprivacy
challenges. Highlighting the challenges will motivate
increased focus on fortifying Big Data infrastructures.
2.4 “Review Paper on Clustering Based Collaborative
Filtering”
Big Data concerns large-volume, complex, growing data
sets with multiple, autonomous sources. In Big Data
applications data collection hasgrowntremendouslyanditis
beyond the ability of commonly used software to capture,
manage, and process that data. The most fundamental
challenge for the Big Data applications is to explore the large
volumes of data and extract useful informationorknowledge
for future actions. Recommender systems (RSs) are
techniques and intelligent applications to assist users in a
decision-making process where they want to choose some
items among set of alternative products or services. RSs
encounter two main challenges for big data application:
1) To make decision within acceptable time.
2) To generate ideal recommendations from so many
services.
This system reports review on collaborative filtering which
uses clustering algorithms. Nowadays Service relevant data
become too big to be effectively processed by traditional
approaches, so one solution to this challenge is Clustering
Based Collaborative Filtering. This approach recruits similar
services in the same cluster to recommend services
collaboratively.
3. PROPOSED SYSTEM
3.1 System Architecture
The proposed system isa modifiedversionoftheexisting
system which addresses major issues such as Brute force
attack or collusion attack. The cloud service providers are
always considered to be dishonest and therefore files on
cloud are encrypted. But If the CSP makes and attack on the
encrypted files too, he can somehow get the sensitivedata of
users and compromise the user privacy. So the proposed
system efficiently analyses this issue and proposes a system
that will maintain security from unauthorized users as well
as dishonest CSPs. Files stored on the cloud are not the
whole, instead files are divided into threshold number of
pieces (threshold i.e. count is decided by users), and each
piece is encrypted and store don separate CSP such that
none of the CSP has the idea whether the file is a complete
file or a piece of file.
The proposed system can be widely made operational on
university clouds in order to store the sorted questions of
question papers and thereby avoid question paper leaks.

Fig -1: Architecture of Proposed System
3.2 ALGORITHM
1. AES ENCRYPTION ALGORITHM- FOR ENCRYPTING FILES
2. CHUNKING ALGORITHM- FOR DIVIDING FILES INTO
PIECES
Step 1: select a file to upload
Step 2: Read the file into buffer reader.
Step 3: For all the data in the buffer reader
Step 4: Read each line till n size is detected.
Step 5: Consider the data till nth byte as a chunk
Step 6: While all the data is read select data till every 10nth
byte as a chunk.
Step 7: Encrypt each chunk (piece) with the AES encryption
algorithm and upload on different CSP.
Step 8: Send the decryption key to the authorized user on
data request.
4. MATHEMATICAL MODEL
4.1 Set Theory
A set is defined as a collection of distinct objects of same
type on class of objects. The objectofasetarecalledelements
or members of the set. Object can be number, alphabet,
names etc.
Example: A = (1, 2, 3, 4, 5)
Fig -2: Union of Set
Union of two sets A and B is defined to be the set of all those
elements which belongs to set A or set B or both and is
denoted by A U B.
User Authentication:
Set (C) = {c0,c1,c2,c3}
C0= Get User Id
C1= Get Cloud Id
C2= Get Data Owner Info
C3= Get the User Privilege Information
C4= Get Key from Hash Table
Data Security
Set (T) = {c1,c2,d0,d1,d2}
d0= Get Data File Name.
d1= Data accessing user id.
d2= Get Cloud id
Union and Intersection of project
Set (P) = {c0,c1,c2,c3 }
Set (t) = {c1,c2,c3,d0,d1,d2}
5. ADVANTAGES
1. The security in terms of CSP is achieved as the CSP does
not know which user stores or divides the file in how many
chunks.
2. The security in terms of attacker users is achieved as the
key is not stored completely, instead its divided into pieces
and the access levels are checked before file access.

6. RESULT AND PERFORMANCE ANALYSIS
Fig -3: Performance Analysis
7. CONCLUSIONS
In this paper it is analyzed the notion of authorized Data
Security is proposed to protect thedata securitybyincluding
differential privileges of users in the unauthorized check. It
also presented several new duplication constructions
supporting authorized unauthorized check in hybrid cloud
architecture, in which unauthorized check isdoneandtoken
of files are generated by private cloud server with private
keys. The designing shows that how the actual flow of this
project. Its constraints on designing of methods to achieve
changeover and evaluation of change over methods.
In this proposed system provide thesecureSecuritywiththe
help of token generation, Secure upload/download of data
and Authorized Data Security is proposedtoprotectthedata
security. Differential privileges of users in the unauthorized
check are also provided. This technique more useful and
accurate technique for Data Security. The current system is
98 Percent better than existing system.
ACKNOWLEDGEMENT
All faith and honor to the HOD for his grace and inspiration.
I would like to thank all my Friends and Family members
They were always been there to support me. I sincerely
thanks to my Department Head, PG coordinatorandall other
staff members to give me the guidelines for this paper.
REFERENCES
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[2] C. Hu, X. Cheng, Z. Tian, J. Yu, K. Akkaya, and L. Sun, “An
attribute based signcryption scheme to secure attribute-
defined multicast communications,” in Secure Comm 2015.
Springer, 2015, pp. 418–435
[3] C. Hu, X. Liao, and X. Cheng, “Verifiable multi-secret
sharing based on LFSR sequences,” Theoretical Computer
Science, vol. 445, 2012.
[4] B. Waters, “Ciphertext-policyattribute-based encryption:
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Public Key Cryptography–PKC 2011, pp. 53–70, 2011.
[5] Cheng Hongbing 1,2, Rong Chunming 3, Hwa ng Kai 4,
Wang Weihong 1, LI Yanyan 1, ”Secure Big Data Storage and
Sharing Scheme for Cloud Tenants ” China Communications
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[6] SHUI YU, ”Big Privacy: Challenges and Opportunities of
Privacy Study in the Age of Big Data”,Digital ObjectIdentifier
10.1109/ACCESS.2016.2577036.
[7] Chunqiang Hu, Hongjuan Li, Xiuzhen Cheng, Xiaofeng
Liao, ”Secure and Ef- ficientdata communicationprotocol for
Wireless Body Area Networks, IEEE TRANSACTIONS ON
MULTI-SCALE COMPUTING SYSTEMS, VOL. , NO. , 11. 2015.
[8] Z. Eslami and J. Z. Ahmadabadi, ”A verifiable multi-secret
sharing scheme based on cellular automata,” Information
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[9] Sheetal Thokal and Vrunda Bhusari, ” Review Paper on
Clustering Based Col- laborative Filtering, Volume 2, Issue
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[10] M. A. Beyer and D. Laney, ”The importance of big data: a
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