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In this literature review journals related to Privacy preservation and file encryption
techniques are revised to get an idea to carry out the process of this project. The revised survey
papers are listed.


J. Xu, W. Wang, J. Pei, X. Wang, B. Shi, and A.W.C. Fu, “Utilitybased Anonymization
using Local Recording”. This paper explained the clustering techniques has been improved or
enhanced to achieve a privacy preservation in localrecoding anonymization. From the utility
privacy preservation perspective the local-recoding anonymization has been studied. It also uses
the top-down counterpart and a bottom-up greedy approach are together pit-forth based on the
cluster size, the agglomerative clustering technique and divisive clustering techniques get
B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu, “Privacy-Preserving Data Publishing:
A Survey of Recent Developments”. In this paper Data privacy preservation has been
investigated extensively, existing approaches for local-recoding anonymization and models for
privacy are reviewed briefly. Also, the research for scalability issues in existing anonymization
approaches are surveyed shortly. To address the local-recoding anonymization as the k member
clustering problem where the cluster size should not be less than k in order to achieve k-
anonymity, For that the simple greedy algorithm are used.
Xu. Zha , D. Won, Chi .Yang, Jini .Chan, “Proximity Aware Local Recording
Anonymization with Map Reduce for Scalable Bigdata Preservation in Cloud”. In this paper
local recording for big data anonymization against proximity privacy sensitive information is
discussed. In proximity preservation used the two pharse clustering approach of t-ancestor
algorithm. This method used to improve the scalability and time efficiency.
Xuyun Zhang, Chi Yang, Surya Nepal, Chang Liu, Wanchun Dou, Jinjun Chen, “A
MapReduce Based Approach of Scalable Multidimensional Anonymization for Big Data
Privacy Preservation on Cloud”, Xuyun Zhang investigated the scalability issue of


multidimensional anonymization over big data on cloud. The main issues of bigdata is
scalability to finding the median of multidimensional partitioning, But ensuring privacy
preservation of large scale data sets still needs extensive investigation, it is integrated into
scalable and cost effective privacy preserving framework based on mapreduce method.
R. Sreedhar, Dm. Uma, “Bigdata Processing with Privacy Map Reduce Cloud”,
According to the author the privacy preservation techniques use k- anonmity approach. This
paper introduces the map reduce framework to anonymize large scale of dataset using two pharse
top down algorithm. In map reduce framework of optimum balanced scheduling method is used
to improve the privacy of sensitive dataset. In privacy preservation map reduce method it use the
TP-TDS approach to improve the scalability of individual dataset in sensitive field.
D.Chha, H.A.Girija, K. K. Raja, “Data Anonymization Technique for Privacy
Preservation on Map Reduce Framework”. Describe the author of data anonymization
technique to hide sensitive data in the cloud to avoid risk. The existing review paper of privacy
preservation used the k-anonymity approach with two pharse top down algorithm. In additional
use the method of I diversity is used to access the data conveniently in the cloud.
N. Ken, Y. Masa , S. Hisa , S. Yoshi , “Privacy Preservation Analysis Technique for
Secure, Cloud Based Bigdata Analysis”. In this paper describe the privacy preservation in the
cloud based on statistical analysis and some secure mechanism. Hitachi has describe a privacy
preserving analysis technique . In this technique is used to analyze data based on sequence steps
for privacy preserving analysis. Encryption technique is used in the common key searchable
method. It provide the efficient access between the user and cloud provider.
S. S. Bhanu, D. Sakthi , “A Review of Privacy Preservation Data Publishing the
Health Care”. In this paper describe the privacy preservation data publishing of electronic
medical record system are used. They are used two different techniques are anonymization and
encryption approach. The healthcare data uses some anonymization approach namely single
anonymization and multiple anonymization technique.
P.Ashi, S.Tejas, J.Srini, D.Sun, “Medical Application of Privacy Preservation by
Bigdata Analysis using Hadoop Map Reduce Framework”. In this paper describe the large
scale of data analysis at optimum response time. The author implement the privacy terms at
medical application by using hadoop frame work. The proposed system is divided into two major


components sensitive disclosure flag and sensitive weight. Classification algorithm is used to
indicate the efficiency of work.
K. Priyanka, P. Sneha, “Securing Personal Health Records in Cloud Using Attribute
Based Encryption”. According to the author his aim is secure access of personal health records
based on attribute based encryption. In PHR scenario there are multiple security mechanism,
particularly CD-ABE and MA-ABE approach are used. The security mechanism is used to
transmit the personal health records securely.
Zhang X, Liu C, Nepal S, Pandey S, Chen J. ” A Privacy Leakage Upper Bound
Constraint-Based Approach for Cost-Effective Privacy Preserving of Intermediate Data
sets in Cloud”. In this paper, proposed an approach that identifies which part is intermediate of
datasets. And its needs to be encrypted. Generate a tree structure based on relationship between
the intermediate datasets to analyze privacy propagation among datasets. Main problem of
existing system is analyze the intermediate dataset. Because it is need the intensive investigation.
Contributions of this paper, planning to investigate privacy. Efficient scheduling of intermediate
datasets in cloud take privacy preserving. Optimized balanced scheduling strategies are expected
to developed highly efficient privacy aware dataset scheduling.
G. Aggarwal, R. Panigrahy, T. Feder, D. Thomas, K. Kenthapadi, S. Khuller, and A. Zhu
published paper on “Achieving Anonymity via Clustering”. This paper explained Existing
clustering approach for local-recoding anonymization mainly concentrate on record linkage
attacks mainly under the k-anonymity privacy model, without any importance to privacy
breaches incurred by sensitive attribute linkage. Relatively propose a constant factor
approximation algorithm for two clustering based anonymization problem, ie, r-GATHER and r-
CELLULAR CLUSTERING, here the centers for clusters are published without generalization
or suppression.

Literature survey is most important part of the thesis that helps to improve the analysis
and it provides many statistic and strategies were followed by various research persons. It gives
multiple angles for a specified technique to analyze the research topic. In this literate review the
concepts are revised and it gives clarity to apply the technique on this research.