Enhancement of k-anonymity algorithm for privacy preservation in social media

  • Authors

    • Aanchal Sharma chandigarh university
    • Sudhir Pathak chandigarh university
    2018-06-05
    https://doi.org/10.14419/ijet.v7i2.27.11747
  • APL, Cluster, Genetic Algorithm (GA), Information Loss, K-Anonymity, Social Media.

  • Abstract

    In recent times, more and more social data is transmitted in different ways. Protecting the privacy of social network data has turn out to be an essential issue. Hypothetically, it is assumed that the attacker utilizes the similar information used by the genuine user. With the knowledge obtained from the users of social networks, attackers can easily attack the privacy of several victims. Thus, assuming the attacks or noise node with the similar environment information does not resemble the personalized privacy necessities, meanwhile, it loses the possibility to attain better utility by taking benefit of differences of users’ privacy necessities. The traditional research on privacy-protected data publishing can only deal with relational data and even cannot applied to the data of social networking. In this research work, K-anonymity is used for providing the security of the sensitive information from the attacker in the social network. K-anonymity provides security from attacker by making the graph and developing nodes degree. The clusters are made by grouping the similar degree into one group and the process is repeated until the noisy node is identified. For measuring the efficiency the parameters named as Average Path Length (APL) and information loss are measured. A reduction of 0.43% of the information loss is obtained.

     

     

  • References

    1. [1] Pham, V. V. H., Yu, S., Sood, K., & Cui, L. (2017). Privacy issues in social networks and analysis: a comprehensive survey. IET Networks.

      [2] Gerbaudo, P. (2018). Tweets and the streets: Social media and contemporary activism. Pluto Press.

      [3] 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. https://doi.org/10.1145/2970030.2970035.

      [4] Zhang, S., Li, X., Liu, H., Lin, Y., &Sangaiah, A. K. (2018). A Privacy-Preserving Friend Recommendation Scheme in Online Social Networks. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2017.12.031.

      [5] Xu, L., Jiang, C., Chen, Y., Wang, J., &Ren, Y. (2016). A framework for categorizing and applying privacy-preservation techniques in big data mining. Computer, 49(2), 54-62. https://doi.org/10.1109/MC.2016.43.

      [6] Francis, J., & Stokes, M. (2012). U.S. Patent No. 8,140,502. Washington, DC: U.S. Patent and Trademark Office.

      [7] Jung, E. K., Levien, R. A., Lord, R. W., Malamud, M. A., Mangione-Smith, W. H., &RinaldoJr, J. D. (2012). U.S. Patent No. 8,203,609. Washington, DC: U.S. Patent and Trademark Office.

      [8] He, Z., Cai, Z., & Yu, J. (2018). Latent-data privacy preserving with customized data utility for social network data. IEEE Transactions on Vehicular Technology, 67(1), 665-673. https://doi.org/10.1109/TVT.2017.2738018.

      [9] Li, J., Yan, H., Liu, Z., Chen, X., Huang, X., & Wong, D. S. (2017). Location-sharing systems with enhanced privacy in mobile online social networks. IEEE Systems Journal, 11(2), 439-448. https://doi.org/10.1109/JSYST.2015.2415835.

      [10] Xiao, X., Chen, C., Sangaiah, A. K., Hu, G., Ye, R., & Jiang, Y. (2017). CenLocShare: a centralized privacy-preserving location-sharing system for mobile online social networks. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.01.035.

      [11] Wang, Y., Cai, Z., Chi, Z., Tong, X., & Li, L. (2018). A differentially k-anonymity-based location privacy-preserving for mobile crowdsourcing systems. Procedia Computer Science, 129, 28-34. https://doi.org/10.1016/j.procs.2018.03.040.

      [12] Diwakar, A. K., Singh, N. K., &Tomar, D. S. (2017, January). End user privacy preservation in social networks against neighborhood attack. In Asia Security and Privacy (ISEASP), 2017 ISEA (pp. 1-9). IEEE. https://doi.org/10.1109/ISEASP.2017.7976991.

      [13] Wei, R., Tian, H., & Shen, H. (2018). Improving k-anonymity based privacy preservation for collaborative filtering. Computers & Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2018.02.017.

      [14] Fei, F., Li, S., Dai, H., Hu, C., Dou, W., & Ni, Q. (2017). A K-Anonymity Based Schema for Location Privacy Preservation. IEEE Transactions on Sustainable Computing. https://doi.org/10.1109/TSUSC.2017.2733018.

      [15] Liu, P., Bai, Y., Wang, L., & Li, X. (2017). Partial k-Anonymity for Privacy-Preserving Social Network Data Publishing. International Journal of Software Engineering and Knowledge Engineering, 27(01), 71-90. https://doi.org/10.1142/S0218194017500048.

      [16] Liu, X., Xie, Q., & Wang, L. (2017). Personalized extended (α, k) anonymity model for privacy preserving data publishing. Concurrency and Computation: Practice and Experience, 29(6). https://doi.org/10.1002/cpe.3886.

      [17] Wang, H., Huang, H., Qin, Y., Wang, Y., & Wu, M. (2017). Efficient Location Privacy-Preserving k-Anonymity Method Based on the Credible Chain. ISPRS International Journal of Geo-Information, 6(6), 163. https://doi.org/10.3390/ijgi6060163.

      [18] Zhang, S., Li, X., Liu, H., Lin, Y., & Sangaiah, A. K. (2018). A Privacy-Preserving Friend Recommendation Scheme in Online Social Networks. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2017.12.031.

      [19] Amardeep singh, Divya bansal,& sanjeev sofat (2014) A Privacy Preserving Techniques in Social Network Data Volume 87 - No.15

      [20] Campan, A., &Truta, T. M. (2009). Data and structural k-anonymity in social networks. In Privacy, Security, and Trust in KDD (pp. 33-54). Springer, Berlin, Heidelberg https://doi.org/10.1007/978-3-642-01718-6_4.

      [21] Zhou, B., & Pei, J. (2011). The k-anonymity and l-diversity approaches for privacy preservation in social networks against neighborhood attacks. Knowledge and Information Systems, 28(1), 47-77. https://doi.org/10.1007/s10115-010-0311-2.

      [22] Zheleva, E., &Getoor, L. (2008). Preserving the privacy of sensitive relationships in graph data. In Privacy, security, and trust in KDD (pp. 153-171). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78478-4_9.

      [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] Kefei Mao, Jie Chen, Jianwei Liu and Mengmeng Wang, ‘Security enhancement on an authentication scheme for privacy preservation in Ubiquitous Healthcare System,’ 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), Harbin, 2015, pp. 885-892.

      [25] A. V. Kishor and S. B. Shrimantrao, ‘Performance enhancement and analysis of privacy preservation using slicing approach over hadoop,’ 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016, pp. 353-357.

  • Downloads

  • How to Cite

    Sharma, A., & Pathak, S. (2018). Enhancement of k-anonymity algorithm for privacy preservation in social media. International Journal of Engineering & Technology, 7(2.27), 40-45. https://doi.org/10.14419/ijet.v7i2.27.11747

    Received date: 2018-04-19

    Accepted date: 2018-05-01

    Published date: 2018-06-05