Privacy Preserving Technique for Mitigating Anonymity Attack in Pervasive Social Networking Applications

  • Authors

    • Nur’ Ayuni binti Adnan
    • Manmeet Mahinderjit Singh
    • Aman Jantan
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23380
  • Pervasive Social Networking (PSN), Privacy preserving technique, Social Network (SN)

  • Abstract

    Pervasive Social Networking (PSN) applications become more popular in the last few years. The uses of PSN applications through mobile devices such as smartphones, tablets will lead to the security and privacy issues. This is because users tend to share their personal information with the third party organizations such as applications in mobile devices. Due to the development of social network, the security and privacy need to be improved as well as others to make sure that all the user’s information is protected securely in social network (SN). In this study, we will focus more on the privacy issues on how to preserve the privacy of user’s data from being known by the third party. The dataset of PSN application will be tested using data mining tool, which is Weka, in order to identify the optimal technique and classifier that can be applied to conceal the information. Then, a new enhanced base learner will be proposed, which is masking technique algorithms will be implemented into the dataset of PSN application at the end of this research.

     

     

  • References

    1. [1] Papadopoulou, E., et al., Combining pervasive computing with social networking for a student environment, in Proceedings of the Twelfth Australasian Symposium on Parallel and Distributed Computing - Volume 152. 2014, Australian Computer Society, Inc.: Auckland, New Zealand. p. 11-19.

      [2] Mokhtar, S.B., L. McNamara, and L. Capra, A middleware service for pervasive social networking, in Proceedings of the International Workshop on Middleware for Pervasive Mobile and Embedded Computing. 2009, ACM: Urbana Champaign, Illinois. p. 1-6.

      [3] C., W. Mobile social networking usage soars[stats]. 2010; Available from: http://mashable.com/2010/03/03/comscore-mobile-stats/.

      [4] Ahn, G.-J., M. Shehab, and A. Squicciarini, Security and privacy in social networks. IEEE Internet Computing, 2011. 15(3): p. 10-12.

      [5] Beach, A., M. Gartrell, and R. Han. Solutions to Security and Privacy Issues in Mobile Social Networking. in 2009 International Conference on Computational Science and Engineering. 2009.

      [6] Sweeney, L., <i>k</i>-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst., 2002. 10(5): p. 557-570.

      [7] Wen, K.S. and M.M. Singh, A Pervasive Social Networking Application: I-NFC enabled Florist Smart Advisor. IOP Conference Series: Materials Science and Engineering, 2016. 160: p. 012091.

      [8] Nafa, et al., Mobile social networking applications. Commun. ACM, 2013. 56(3): p. 71-79.

      [9] Verykios, V.S., et al., State-of-the-art in privacy preserving data mining. SIGMOD Rec., 2004. 33(1): p. 50-57.

      [10] A., V.H.a.G., A Survey: Privacy Preservation Techniques in Data Mining International Journal of Computer Applications 2015. Volume 119 - Number 4 p. 20-26.

      [11] Xie, Q. and U. Hengartner. Privacy-preserving matchmaking For mobile social networking secure against malicious users. in 2011 Ninth Annual International Conference on Privacy, Security and Trust. 2011.

      [12] Ajami, R., N.A. Qirim, and N. Ramadan, Privacy Issues in Mobile Social Networks. Procedia Computer Science, 2012. 10: p. 672-679.

      [13] Phalnikar, S.M.N.a.R., k-Anonymization using Multidimensional Suppression for Data De-identification. International Journal of Computer Applications, 2012. 60(11).

      [14] Game, V.N.a.P., Classification Tree-Based k-Anonymity with Masking Operations to Enhance Data Utility. Proc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC, 2014.

      [15] Mandapati, S., R.B. Bhogapathi, and M.V.P.C.S. Rao, Classification via Clustering for Anonym zed Data. International Journal of Computer Network and Information Security, 2014. 6(3): p. 52-58.

      [16] Brush, A.J.B., J. Krumm, and J. Scott, Exploring end user preferences for location obfuscation, location-based services, and the value of location, in Proceedings of the 12th ACM international conference on Ubiquitous computing. 2010, ACM: Copenhagen, Denmark. p. 95-104.

      [17] Vania Bogorny, A.T.P., Paulo Martins Engel, Luis Otavio Alvares, Weka-GDPM – Integrating Classical Data Mining Toolkit to Geographic Information Systems.

      [18] Luis Otavio Alvares, A.L.P., Gabriel Oliveira, Vania Bogorny, Weka-STPM: from trajectory samples to semantic trajectories.

      [19] Moore Jr, R.A., CONTROLLED DATA-SWAPPING TECHNIQUES FOR MASKING PUBLIC USE MICRODATA SETS. Statistical Research Division, 1996.

      [20] Manmeet Mahinderjit Singh, N.F.A.N., Privacy Preserving Techniques for Ambient Intelligence System: A Case of AMbient Intelligence Smart Home (AMISHA), in School of Computer Science. 2016, Universiti Sains Malaysia. p. 98.

      [21] Madria, M.T.a.S., Sensor networks: an overview. 2003.

      [22] Inan, A., M. Kantarcioglu, and E. Bertino. Using Anonymized Data for Classification. in 2009 IEEE 25th International Conference on Data Engineering. 2009.

      [23] al, G.O.e., Privacy Preserving in Data Mining – Experimental Research on SMEs Data 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics 2011.G. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),†in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64.


  • Downloads

  • How to Cite

    Ayuni binti Adnan, N., Mahinderjit Singh, M., & Jantan, A. (2018). Privacy Preserving Technique for Mitigating Anonymity Attack in Pervasive Social Networking Applications. International Journal of Engineering & Technology, 7(4.31), 272-279. https://doi.org/10.14419/ijet.v7i4.31.23380

    Received date: 2018-12-07

    Accepted date: 2018-12-07

    Published date: 2018-12-09