A SURVEY ON MAP REDUCE FRAMEWORK FOR CLUSTERING SECURITY

AUTHORS

S. Naga Mallik Raj,Vignan’s Institute of Information Technology, Visakhapatnam, AP, India
S. Neeraja,Department of CSE, Pydah College of Engineering and Technology, Visakhapatnam, AP, India

ABSTRACT

For keeping up the legitimacy, security and secrecy of bigger dataset are re-appropriated to the cloud in the encoded organization. Distributed storage gives information the board and lessens the expenses. We Clearly expressed the Map decrease structure alongside precedent Bear, Deer, River and Car. We clarified significance of grouping security alongside Partitioning Clustering and Hierarchical Clustering. Various approaches among that two-party key issuing show that can guarantee that neither key master nor cloud authority association can deal the whole secret key of a customer independently

 

KEYWORDS

DFG, MapReduce, clustering, cloud computing

REFERENCES

[1]     Dongxi Liu, Elisa Bertino, and Xun Yi, “Privacy of outsourced k-means clustering,” the 9th ACM b Symposium on Information, Computer and Communications Security, ASIA CCS ’14, pp.123-134, New York, NY, USA, (2014) [DOI:10.1145/2590296.2590332](CrossRef)(Google Scholar)
[2]     Yongge Wang, “Notes on two fully homomorphic encryption schemes without bootstrapping” Cryptology ePrint Archive, Report 2015/519, (2015).
[3]     B. Yao, F. Li, and X. Xiao, “Secure nearest neighbor revisited,” Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pp.733-744, April (2013) [DOI: 10.1109/ICDE.2013.6544870](CrossRef)(Google Scholar)
[4]     Sen Su, Yiping Teng, Xiang Cheng, Yulong Wang, and Guoliang Li, “Privacy-preserving top-k spatial keyword queries over the outsourced database,” In Proceedings of the 20th International Conference on Database Systems for Advanced Applications, DASFAA’15, pp.589-608, (2015) [DOI: 10.1007/978-3-319-18120-2_34](CrossRef)(Google Scholar)
[5]     Jiawei Yuan and Shucheng Yu, “Privacy preserving back-propagation neural network learning made practical with cloud computing,” IEEE Transactions on Parallel and Distributed Systems, Vol.25, No.1, pp.212-221, (2014) [DOI: 10.1109/TPDS.2013.18](CrossRef)(Google Scholar)
[6]     Amudha. S, “An Overview of Clustering Algorithm in Data Mining”, International Research Journal of Engineering and Technology (IRJET), Vol.3, No.12, Dec (2016)
[7]     Garima, Hina Gulati and P.K.Singh, “Clustering Techniques in Data Mining: A Comparison”, International Conference on Computing for Sustainable Global Development (INDIACom), pp. 410-415, IEEE (2015).
[8]     Sukhvir Kaur, “Survey of Different Data Clustering Algorithms”, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, pp.584-588, May- (2016).

CITATION

  • APA:
    Raj,S.N.M.& Neeraja,S.(2019). A SURVEY ON MAP REDUCE FRAMEWORK FOR CLUSTERING SECURITY. International Journal of Private Cloud Computing Environment and Management, 6(1), 9-16. http://dx.doi.org/10.21742/IJPCCEM.2019.6.1.02
  • Harvard:
    Raj,S.N.M.and Neeraja,S.(2019). "A SURVEY ON MAP REDUCE FRAMEWORK FOR CLUSTERING SECURITY". International Journal of Private Cloud Computing Environment and Management, 6(1), pp.9-16. doi:http://dx.doi.org/10.21742/IJPCCEM.2019.6.1.02
  • IEEE:
    [1]S.N.M.Rajand S.Neeraja, "A SURVEY ON MAP REDUCE FRAMEWORK FOR CLUSTERING SECURITY". International Journal of Private Cloud Computing Environment and Management, vol.6, no.1, pp.9-16, Sep. 2019
  • MLA:
    Raj S. Naga Mallikand Neeraja S.. "A SURVEY ON MAP REDUCE FRAMEWORK FOR CLUSTERING SECURITY". International Journal of Private Cloud Computing Environment and Management, vol.6, no.1, Sep. 2019, pp.9-16, doi:http://dx.doi.org/10.21742/IJPCCEM.2019.6.1.02

ISSUE INFO

  • Volume 6, No. 1, 2019
  • ISSN(p):2205-8478
  • ISSN(o):2207-3973
  • Published:Sep. 2019

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