Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
General Research Article

Optimal Privacy Preserving Technique Over Big Data Analytics Using Oppositional Fruit Fly Algorithm

Author(s): Ajmeera Kiran* and Vasumathi Devara

Volume 13, Issue 2, 2020

Page: [283 - 295] Pages: 13

DOI: 10.2174/2213275911666181119113913

Price: $65

Abstract

Background: Big data analytics is the process of utilizing a collection of data accompanied on the internet to store and retrieve anywhere and at any time. Big data is not simply a data but it involves the data generated by variety of gadgets or devices or applications.

Objective: When massive volume of data is stored, there is a possibility for malevolent attacks on the searching data are stored in the server because of under privileged privacy preserving approaches. These traditional methods result in many drawbacks due to various attacks on sensitive information. Hence, to enhance the privacy preserving for sensitive information stored in the database, the proposed method makes use of efficient methods.

Methods: In this manuscript, an optimal privacy preserving over big data using Hadoop and mapreduce framework is proposed. Initially, the input data is grouped by modified fuzzy c means clustering algorithm. Then we are performing a map reduce framework. And then the clustered data is fed to the mapper; in mapper the privacy of input data is done by convolution process. To validate the privacy of input data the recommended technique utilizes the optimal artificial neural network. Here, oppositional fruit fly algorithm is used to enhancing the neural networks.

Results: The routine of the suggested system is assessed by means of clustering accuracy, error value, memory, and time. The experimentation is performed by KDD dataset.

Conclusion: A result shows that our proposed system has maximum accuracy and attains the effective convolution process to improve privacy preserving.

Keywords: Privacy preserving, fuzzy c means, artificial neural network, fruit fly, convolution process, algorithm.

Graphical Abstract

[1]
M. Blanton, "Achieving full security in privacy-preserving data mining", In: Proceeding of IEEE International Conference on Social Computing. Oct 2011, pp. 925-934
[2]
B. Peng, X. Geng, and J. Zhang, "Combined data distortion strategies for privacy-preserving data mining", In: Proceeding of IEEE International Conference on Advanced Computer Theory and Engineering. Aug 2010, Vol. 1, pp. 572-576.
[3]
X. Lei, C. Jiang, J. Wang, J. Yuan, and Y. Ren, "Information security in big data: Privacy and data mining", IEEE Access, vol. 2, pp. 1149-1176, 2014.
[4]
V.S. Verykios, E. Bertino, I.N. Fovino, L.P. Provenza, Y. Saygin, and Y. Theodoridis, "State-of-the-art in privacy preserving data mining", In: Proceeding of SIGMOD Record. March 2004, Vol. 33,No. 1, pp. 50-57.
[5]
J. Vaidya, B. Shafiq, W. Fan, D. Mehmood, and D. Lorenzi, "A random decision tree framework for privacy-preserving data mining", IEEE Trans. Depend. Secure Comput., vol. 11, no. 5, pp. 399-411, 2014.
[6]
M.T.M. Saleem, and A.P. Kankale, "Privacy preserving and data mining in big data", Int. Res. J. Engin. Tech., vol. 3, no. 10, pp. 197-201, 2016.
[7]
R. Mendes, and J.P. Vilela, "Privacy-preserving data mining: methods, metrics, and applications", IEEE Access, vol. 5, pp. 10562-10582, 2017.
[8]
C.C. Aggarwal, Data Mining: The Textbook.New York, NY, USA, . Springer, 2015
[9]
G.A. Afzali, and S. Mohammadi, "Privacy preserving big data mining: association rule hiding using fuzzy logic approach", IET Inf. Secur., vol. 12, no. 1, pp. 15-24, 2017.
[10]
Z. Xuyun, L. Chang, and N.S. Surya, "A hybrid approach for scalable subtree anonymization over big data using MapReduce on cloud", JCSS, vol. 80, no. 5, pp. 1008-1020, 2014.
[11]
C.L.C. Philip, and C.Y. Zh, "Data-intensive applications, challenges, techniques and technologies: A survey on big data", Inf. Sci., vol. 275, pp. 314-347, 2014.
[12]
W. Wang, B. Deng, and Z. Li, "Application of oblivious transfer protocol in distributed data mining with privacy-preserving", The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007). Nov, 2007, pp. 283-285.
[13]
W. Xindong, Z. Xingquan, and W. Gong-Qing, "Data mining with big data", IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97-107, 2014.
[14]
Shengyi. Pan, "T.Morris, and U. Adhikari, “Developing a hybrid intrusion detection system using data mining for power systems", IEEE Transactions on Smart Grid, vol. 6, pp. 3104-3113, 2015.
[15]
C.Y. Lin, "A reversible data transform algorithm using integer transform for privacy-preserving data mining", J. Syst. Softw., vol. 117, pp. 104-112, 2016.
[16]
P.S. Wang, F. Lai, H-C. Hsiao, and J-L. Wu, "Insider collusion attack on privacy-preserving kernel-based data mining systems", IEEE Access, vol. 4, pp. 2244-2255, 2016.
[17]
L. Li, R. Lu, K.K.R. Choo, A. Datta, and J. Shao, "Privacy-preserving-outsourced association rule mining on vertically partitioned databases", IEEE T. Info. Foren. Sec., vol. 11, no. 8, pp. 1847-1861, 2016.
[18]
K. Liu, H. Kargupta, and J. Ryan, "Random projection-based multiplicative data perturbation for privacy preserving distributed data mining", IEEE Trans. Knowl. Data Eng., vol. 18, no. 1, pp. 92-106, 2006.
[19]
L.A. Dunning, and R. Kresman, "Privacy preserving data sharing with anonymous ID assignment", IEEE T. Info. Foren. Sec. Vol. 8,No.2, pp .402-413, 2013.
[20]
K. Chen, and L. Liu, "Privacy-preserving multiparty collaborative mining with geometric data perturbation", IEEE Trans. Parallel Distrib. Syst., vol. 20, no. 12, pp. 1764-1776, 2009.
[21]
N.S. Bushra, and A. Chandrasekar, "Privacy preservation on big data using Pk-Anonymization", IJIRCST, vol. 3, no. 11, pp. 11937-11942, 2015.
[22]
B.B. Mehta, and U.P. Rao, "Privacy preserving big data publishing: a scalable k-anonymization approach using MapReduce", IET Softw., vol. 11, no. 5, pp. 271-276, 2017.
[23]
H.R. Tizhoosh, "Opposition-based learning: A new scheme for machine intelligence", International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), vol. 1, pp. 695-701. 2008

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy