Neural Network based Privacy Preservation Data Mining for Social Network Sites
DOI:
https://doi.org/10.29070/eaa69n98Keywords:
Information loss, K-anonymity, Preservation, Clustering, APL, Neural NetworksAbstract
Due to increasing use of Online Social Networks (OSNs) applications such as Facebook, Twitter,etc., several research challenges related to security and privacy of OSN users introduced. The end users ofOSNs expecting that social networks should strong enough to preserve their private data secure from theattackers. In this research work, we introduce the novel data mining based framework to protect the OSNsfrom various privacy violation concerns. Risk of disclosure of individual’s confidential information haverisen tremendously due to widening of social network and publication of its data. From securityperspective privacy retaining becomes mandatory prior to service providers publish networkinformation. Recently, preservation of privacy in data of social networks has become most challengingand concerning problem as it has caught our lives in a dramatic way. Various methods ofanonymizationexist thathelps in retaining privacy of social networking.By developing graph and nodesdegree, k-Anonymity and among all available techniques is an utmost one that assist in deliveringsecurity of information on internet. With major manipulation in editing of node techniques in thisresearch paper, improvement of K-anonymity has been explained. With integration of same degree inone group, clusters are developed and processes are repeated untillrecognition and identification ofnoisy data is done. For minimizing node misplacement in groupsan Advanced Cuckoo Search iscommenced and processed. To cross verifystructure and for reducing node miss placement in groupsoutcome of Cuckoo Searches are combined with Feed Forward Back Propagation Neural Networks. Wehave computed the Information Loss and Average Path Length of proposed model. These resultsshowed that the reduction in these parameters to a good extent compared to other implementations.These values of Information Loss and Average Path Length in case of a network with 9 nodes areobtained as 0.24 and 32.2 respectively.Downloads
References
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.
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.
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.
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
Francis, J., & Stokes, M. (2012). U.S. Patent No. 8,140,502.Washington, DC: U.S. Patent and Trademark Offic
P. MohanaChelvan and K. Perumal, “Stable FeatureSelection 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.
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.
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.
Aanchal Sharma and SudhirPathak, “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.
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.
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
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
Tripathy, B. K., Sishodia, M. S., Jain, S., &Mitra, A. (2014). Privacy and Anonymization in Social Networks. Intelligent Systems Reference Library, 243–270
Chester, S., Kapron, B. M., Srivastava, G., &Venkatesh, S. (2013). Complexity of social network anonymization. Social Network Analysis and Mining, 3(2), 151-166.
Lin, K. P., & Chen, M. S., “On the design and analysis of the privacy-preserving SVM classifier”, IEEE Transactions on Knowledge and Data Engineering, 23(11), (2010). 1704-1717.
Okada, R., Watanabe, C., & Kitagawa, H., “A k-anonymization algorithm on social network data that reduces distances between node”, In 2014 IEEE 33rd International Symposium on Reliable Distributed Systems Workshops, (2014). (pp. 76-81). IEEE.
Tsai, Y. C., Wang, S. L., Kao, H. Y., & Hong, T. P., “Edge types vs privacy in K-anonymization of shortest paths”, Applied Soft Computing, (2015).31, 348-359.
Maheshwarkar, B., Patidar, P., Rawat, M. K., &Maheshwarkar, N., “K-AMOA: K-Anonymity model for multiple overlapped attributes”, In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies. (2016). (p. 83). ACM.
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.
Tsai, Y. C., Wang, S. L., Song, C. Y., & Ting, I. H, “Privacy and Utility Effects of k-anonymity on Association Rule Hiding, .(2016).
Meden, Blaž, et al. "k-Same-Net: k-Anonymity with generative deep neural networks for face deidentification." Entropy 20.1 (2018): 60.
Song, F., Ma, T., Tian, Y., & Al-Rodhaan, M., “A new method of privacy protection: random k- anonymous”, (2019). IEEE Access.
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
Shailaja, G. K., &Rao, C. G., “Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation”, (2019), Journal of Intelligent Systems.
Namdarzadegan, M., &Khafaei, T., “Privacy Preserving in Social Networks Using Combining Cuckoo Optimization Algorithm and Graph Clustering for Anonymization”, Asian Journal of Research in Computer Science, (2019).1-12.
