IRJET - A Secure Access Policies based on Data Deduplication System

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 02 | Feb 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 3155
A SECURE ACCESS POLICIES BASED ON DATA DEDUPLICATION SYSTEM
Gayathri.S1, Ragavi.P2, Srilekha.R3
1Asst Professor, Dept. of Computer Science and Engineering, Jeppiaar SRR Engineering college, Padur.
2,3Final year student, Dept. of Computer Science and Engineering, Jeppiaar SRR Engineering college, Padur.
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Abstract - Deduplication techniques are used to a back up
data on disk and minimize the storage overhead by detecting
and eliminating redundancy among data. It is crucial for
eliminating duplicate copies of identical data to save storage
space and network bandwidth. It presents an attribute-based
storage system with secure data deduplication access policies
in a hybrid clouds settings, using public cloud and private
cloud. Private cloud is used to detect duplication and a public
cloud maintains the storage. Instead of keeping data copies in
multiple with the similar content, in this system removes
surplus data by keeping only one physical copy and referring
other redundant data to that copy. User access policies defines
each of such copy, the user will upload the file with access
policies. Then similar file with the separate access policies are
set the particular file to replace the reference. The user’s
private key is associated with attribute set and a message is
encrypted under an access policy over a set of attributes. The
user can decrypt a cipher text with private key if the set of
attributes satisfies the accesspolicyassociatedwiththiscipher
text. Our system has two advantages. Thetwolevelofchecking
file is file level deduplication and signature match checking. It
reduced the time and cost in uploading and downloadingwith
storage space in Hadoop system.
KeyWords: Hadoop Software, Ciphertext, Deduplication,
Tomcat.
1. INTRODUCTION
Hadoop software library that permits for the distributed
processing of huge data setacrossclusterofcomputersusing
simple programming models. It is designed to proportion
from single server to the thousandofmachines, eachoffering
local computation and storage. It makes Use of the
commodity hardware Hadoop is very Scalable and Fault
Tolerant. This provides resource management and
scheduling for user applications; and Hadoop Map Reduce,
which provides the programming model used to tacklelarge
distributed data processing mapping data and reducing it to
a result. Big Data in most companies are processed by
Hadoop by submitting the roles to Master. Thistypeofusage
is best-suited to highly scalable public cloud services; The
Master distributes the job to its cluster and processmapand
reduces tasks sequentially. But nowadays the growing data
and the competition between Service Providers lead to the
increased submission of jobs to the Master. This Concurrent
job submission on Hadoop forces us to do Schedule on
Hadoop Cluster so that the response time will be acceptable
for each job.
2. PROPOSED SYSTEM
In this paper, we present an attribute-based storage system,
which uses the ciphertext-policy attribute-based encryption
(CP-ABE) and to support securededuplication.To enablethe
deduplication and distributed storage of the data across
HDFS. And then using two way cloudinourstoragesystemis
built under a hybrid cloud architecture, where a private
cloud manipulates the computation of the user and a public
cloud manages the storage. The private cloud is given a
trapdoor key related to the corresponding ciphertext, with
which it can transfer the ciphertext over one access policy
into ciphertexts of the same plaintext under the otheraccess
policies without being conscious of the underlyingplaintext.
After receiving a storage request from the user, the private
cloud first checks the validity of the upload item through the
attached proof. If the proof is valid, the private cloud runs a
tag matching algorithm to ascertain whether an equivalent
data underlying the ciphertext has been stored
3. HARDWARE AND SOFTWARE SPECIFICATION
HARDWARE REQUIREMENTS:
Hard disk : 500 GB and above.
Processor : i3 and above.
Ram : 4GB and above.
SOFTWARE REQUIREMENTS:
Operating System : Windows 7 and above (64-bit).
Java Version : JDK 1.7
Web Server :Tomcat 6.20
Web Server :Tomcat 7.0.11
Storage : Hadoop 2.7
4. TECHNOLOGY USED
 JAVA
 Cloud Computing
 JAVA Platform
5. APACHE TOMCAT SERVER
Apache Tomcat (formerly under the ApacheJakarta Project;
Tomcat is now a top level project) could also be an internet
container developed at the Apache Software Foundation.
Tomcat implements the servlet and thusthe JSPspecifyfrom
Sun Microsystems, providing an environment forJava torun

in cooperation with an online server. It adds tools for
configuration andmanagement butalsocanbeconfiguredby
editing configuration files that are normally XML-formatted.
Because Tomcat includes its own HTTP serverinternally,it's
also considered a standalone web server.
5.1 Purpose
The main aim of this project is to realize newdistributed de-
duplication systems we present an attribute-based storage
system with secure deduplication during a hybrid cloud
setting with higher reliability.
5.2 Project Scope
In this Deduplication techniques are employed to backup
data and minimize network of the data and storage of an
data overhead by detecting and eliminating redundancy
among data from the storage area.so which is crucial for
eliminating duplicate copies of identical data so as to save
lots of space for storing and network bandwidth.Wepresent
an attribute-based storage system with secure access
policies data deduplication in a hybrid cloud. Where a
personal cloud is liable for duplicate detection and a public
cloud manages the storage. Instead of keeping multiple data
copy with an equivalent content, during the technique
eliminate redundant data by keeping only physical copyand
referring other redundant datas thereto copy.
6. Algorithms used
 MD5 algorithm.
 Base 64.
 RSA algorithm.
7. SYSTEM DESIGN
7.1 Uploading a File
In this module, cloud user first register the user details and
then login the user credential details. Once user name and
password is valid open the user profile screen are going to
be displayed. A user is an entity whowantstooutsourcedata
storage to the HDFS Storage and access the info later. A user
register to the HDFS storage with necessaryinformationand
login the page for uploading the file. User chooses the file of
data and uploads to Storage in the given space where the
HDFS store the file in rapid storage system and file level
deduplication is checked.
7.2 Mastering File to HDFS Storage
Tag the file by using MD5 message-digest algorithm is
cryptographic hash function producing a 128-bit hash value
typically expressed in text format as 32 digit hex value in
order that files of same are deduplicated. Chunking the file
chosen for the fixed size given of data andgeneratingtags for
each blocks chunked. Then generate convergent keys for
every blocks split to verify block level deduplication.Provide
filename and password for file authorization in future. This
Encrypt the blocks by Triple encoding Standard (3DES)
algorithm. Here the plain text is encoded triple times with
convergent key then while decoding the primary content it
also needs an equivalent key to decode again by triple times.
Finally the first content is encrypted as cipher text and
stored in slave system. Blocks arestoredinDistributedHDFS
Storage Providers.
7.3 Enabling Deduplication Method
After encrypting the convergent keys are securely shared
with slave machines provider toKeyManagementmachines.
Key management slave checks duplicate copies of an data to
convergent keysinKMCSP. KeyManagementslavemaintains
Comma Separated Values file to see proof of verification of
data and store keys secure. Various users who share the
common keys are referred by their own ownership. User
request for deletion definitely got to prove to proof of
ownership to delete own contents.
7.4 Hash Value Based Decryption
The final model user request for downloading their
document which they need to upload in HDFS storage. This
download request needs proper ownership of the data
verification of the document here we create the ownership
by unique tag generated by MD5 algorithm and verifies
existing tag of user. After verification the original content of
the data is decrypted by requesting the Distributed HDFS
storage where HDFS storage request key management slave
for keys to decrypt the given data and finally the original
content is received by the user. The delete request will
delete only the reference of the content shared by common
users, and not the whole content.
Fig -1: Block diagram
8. CONCLUSIONS
In this project, the new distributed deduplication systems
with file-level and fine-grained block-level data
deduplication, higher reliability inwhichthedata chunks are
distributed across HDFSstorage,reliablekeymanagementin
secure deduplication and security of tag consistency and

integrity were achieved. The purpose of software
requirements specification is to provide a detailedoverview
of the software project and its purpose and its parameters
and goals. This describes the project target audience and its
user interface, hardware and software requirements of the
given data. It is defined how the client, team and audience
see the project and its functionality.
9. REFERENCES
[1] D. Quick, B. Martini, and K. R. Choo, Cloud Storage
Forensics. Syngress Publishing / Elsevier, 2014. [Online].
Available:http://www.elsevier.com/books/cloud-
storageforensics/ quick/978-0-12-419970-5
[2] K. R. Choo, J. Domingo-Ferrer, and L. Zhang, “Cloud
cryptography: Theory, practice and future research
directions,” Future Generation Comp. Syst., vol. 62, pp. 51–
53, 2016.
[3] K. R. Choo, M. Herman, M. Iorga, and B. Martini, “Cloud
forensics: State-of-the-art and future directions,” Digital
Investigation, vol. 18, pp. 77–78, 2016.
[4] Y. Yang, H. Zhu, H. Lu, J.Weng, Y. Zhang, and K. R. Choo,
“Cloud based data sharing with fine-grained proxy re-
encryption,” Pervasive and Mobile Computing, vol. 28, pp.
122–134, 2016.
[5] D. Quick and K. R. Choo, “Google drive: Forensic analysis
of data remnants,” J. Network and Computer Applications,
vol. 40, pp. 179– 193, 2014.
[6] A. Sahai and B. Waters, “Fuzzy identity-based
encryption,” in Advances in Cryptology - EUROCRYPT 2005,
24th Annual International Conference on the Theory and
ApplicationsofCryptographicTechniques,Aarhus,Denmark,
May 22-26, 2005, Proceedings, ser. Lecture Notes in
Computer Science, vol. 3494. Springer, 2005, pp. 457–473.

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