IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 2, February 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Enhancing Privacy Preservation Using Hybrid Approach Of K-anonymity, Artificial Bee Colony And Neural Network

[Full Text]

 

AUTHOR(S)

Shivani Sharma, Sachin Ahuja

 

KEYWORDS

Privacy preservation, K-anonymity, Neural Network, ABC, APL, Information Loss.

 

ABSTRACT

The rising popularity of social networks has also raised the risk adjoining the dissemination of the user’s personal information over the network. This has raised the demand of privacy protection. Privacy preservation is the rising issue in the social networks that are the hot spots where information theft instances are very common. The present approach focuses the protection of sensitive information based on k-anonymity. K-anonymity is one of the most popular approaches privileged by graphs and nodes functionality. The proposed study is based on the enhancement of k-anonymity by focusing at node level to address the privacy protection issue. The process involves first identification of sensitive nodes and then applying optimization techniques. At this step, authors have introduced Neural Network (NN) and Artificial Bee Colony (ABC) in order to reduce the node miss placement in the groups. The study is evaluated against k-anonymity on small and larger datasets in terms of average path length and information loss. Comparative analyses have shown APL reduction of 1.636 and 1.371 is achieved using ARNET and SDFB datasets over 900 nodes. Additionally, optimization also resulted in average information loss reduction of 0.57% and 8.95% was observed for small and larger datasets.

 

REFERENCES

[1] A. Kaur, “A hybrid approach of privacy preserving data mining using suppression and perturbation techniques”, In International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, pp. 306-311,2017.
[2] Keküllüoğlu, D., Kökciyan, N., &Yolum, P. (2016, August). Strategies for privacy negotiation in online social networks. In Proceedings of the 1st International Workshop on AI for Privacy and Security (p. 2). ACM.
[3] D. Patel and R. Kotecha, “Privacy Preserving Data Mining: A Parametric Analysis”, In Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Advance in Intelligent Systems and Computing, Vol. 516, pp. 139-149, 2017.
[4] A. Campan and T.M. Truta, “A Clustering Approach for Data and Structural Anonymity in Social Networks,” In Privacy, Security, and Trust in KDD Workshop (PinKDD), 2008
[5] Francis, J., & Stokes, M. (2012). U.S. Patent No. 8,140,502. Washington, DC: U.S. Patent and Trademark Office
[6] P. MohanaChelvan and K. Perumal, “Stable Feature Selection with Privacy Preserving Data Mining Algorithm”, Advanced Informatics for Computing Research. Communications in Computer and Information Science, Springer. Singapore, Vol. 712, pp 227-237, 2017.
[7] Y. Song, P. Karras, Q. Xiao and S. Bressan, “Sensitive Label Privacy Protection on Social Network Data”, IEEE transactions on knowledge and data engineering, Vol.25, No.3, pp 562-571, 2013.
[8] Zhou and J. Pei, “The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks”, Knowledge and Information Systems, Vol.28, No.1, pp 47–77, 2010.
[9] K. Ilavarasi and B. Sathiyabhama, “An evolutionary feature set decomposition based anonymization for classification workloads: Privacy Preserving Data Mining”, Cluster Computing, Vol. 20, No. 4, pp 3515–3525, 2017.
[10] G. Priyanka, P. Darshana and Kotecha Radhika, “Privacy-Preserving Associative Classification”, In International Conference on Information and Communication Technology for Intelligent Systems. Smart Innovation, Systems and Technologies, Springer, Cham, Vol. 2, pp.245-251, 2017.
[11] X. Wu, X.Ying, K. Liu and L. Chen, “A survey of privacy-preservation of graphs and social networks”, In Managing and mining graph data, Springer, Boston, MA, pp. 421-453, 2010.
[12] K. LeFevre, D. J. DeWitt and R. Ramakrishnan, “Mondrian Multidimensional K Anonymity”, In IEEE International Conference of Data Engineering, Vol. 25, pp.1-11,2006.
[13] B. C. M. Fung, Y. Jin and J. Li, “Preserving privacy and frequent sharing patterns for social network data publishing”. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013), Niagara Falls, ON, pp. 479-485, 2013.
[14] Mingxuan Yuan and Lei Chen, “Protecting Sensitive Labels in Social Network Data Anonymization”, IEEE transactions on knowledge and data engineering, Vol. 25, No. 3, 2013.
[15] Tripathy, B. K., Sishodia, M. S., Jain, S., &Mitra, A. (2014). Privacy and Anonymization in Social Networks. Intelligent Systems Reference Library, 243–270
[16] Narayanan and V. Shmatikov, “De-Anonymizing Social Networks”, Proc. IEEE 30th Symp. Security and Privacy, pp. 173-187, 2009.
[17] Z. He, Z. Cai and J. Yu, “Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data”, In IEEE Transactions on Vehicular Technology, Vol. 67, No. 1, pp. 665-673, Jan. 2018.
[18] D. Yin, Y. Shen and C. Liu, “Attribute Couplet Attacks and Privacy Preservation in Social Networks”, in IEEE Access, Vol. 5, pp. 25295-25305, 2017.
[19] Q. Wang, Y. Zhang, X. Lu, Z. Wang, Z. Qin and K. Ren, “Real-Time and Spatio-Temporal Crowd-Sourced Social Network Data Publishing with Differential Privacy”, In IEEE Transactions on Dependable and Secure Computing, Vol. 15, No. 4, pp. 591-606, 2018.
[20] W. Feng, Z. Yan and H. Xie, “Anonymous Authentication on Trust in Pervasive Social Networking Based on Group Signature”, In IEEE Access, Vol. 5, pp. 6236-6246, 2017.
[21] Zhang, S., Li, X., Tan, Z., Peng, T., & Wang, G. (2019). A caching and spatial K-anonymity driven privacy enhancement scheme in continuous location-based services. Future Generation Computer Systems, 94, 40-50.
[22] Aanchal Sharma and Sudhir Pathak, “Enhancement of k-anonymity algorithm for privacy preservation in social media”, International Journal of Engineering & Technology, Vol. 7, No. 2.27, pp.40-45, 2018.
[23] Bhaladhare, P. R., &Jinwala, D. C. (2016). Novel Approaches for Privacy Preserving Data Mining in k -Anonymity Model. J. Inf. Sci. Eng., 32(1), 63 -78
[24] Tsai, Y.-C., Wang, S.-L., Kao, H.-Y., & Hong, T.-P. (2015). Edge types vs privacy in K-anonymization of shortest paths. Applied Soft Computing, 31, 348–359. doi:10.1016/j.asoc.2015.03.005
[25] Chester, S., Kapron, B. M., Srivastava, G., &Venkatesh, S. (2013). Complexity of social network anonymization. Social Network Analysis and Mining, 3(2), 151-166.