Preventing Private Information Inference Attacks on Social Networks

Authors(1) :-Syeda Meraj Bilfaqih

On-line social networks, such as Facebook, are increasingly utilized by many people. These networks allow users to publish details about them-selves and connect to their friends. Some of the information revealed inside these networks is meant to be private. Yet it is possible that corporations could use learning algorithms on released data to predict undisclosed private information. In this paper, we explore how to launch inference at-tacks using released social networking data to predict undisclosed private information about individuals. We then devise three possible sanitization techniques that could be used in various situations. Then, we explore the e?ectiveness of these techniques by implementing them on a dataset obtained from the Dallas/Fort Worth, Texas network of the Facebook social networking application and attempting to use methods of collective inference to discover sensitive attributes of the data set. We show that we can decrease the e?ectiveness of both local and relational classification algorithms by using the sanitization methods we described. Further, we discover a problem domain where collective inference degrades the performance of classification algorithms for determining private attributes.

Authors and Affiliations

Syeda Meraj Bilfaqih
Computer Science, King Khalid University, Saudi Arabia

Social Networks, Markov Networks, SVM, Learning Algorithm

  1. L. Backstrom, C. Dwork, and J. Kleinberg. Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In WWW ‟07: Proceedings of the 16th international conference on World Wide Web, pages 181–190, New York, NY, USA, 2007. ACM.
  2. Facebook Beacon, 2007.
  3. R. Gross, A. Acquisti, and J. H. Heinz. Information revelation and privacy in online social networks. In WPES ‟05: Proceedings of the 2005 ACM workshop on Privacy in the electronic society, pages 71–80, New York, NY, USA, 2005. ACM Press.
  4. M. Hay, G. Miklau, D. Jensen, P. Weis, and S. Srivastava. Anonymizing social networks. Technical Report 07-19, University of Massachusetts Amherst, 2007.
  5. J. He, W. Chu, and V. Liu. Inferring privacy information from social networks. In Mehrotra, editor, Proceedings of Intelligence and Security Informatics, volume LNCS 3975, 2006.
  6. T. Joachims. Training linear SVMs in linear time. In ACM SIGKDD In-ternational Conference On Knowledge Discovery and Data Mining (KDD), pages 217 – 226, 2006.
  7. H. Jones and J. H. Soltren. Facebook: Threats to privacy. Technical report, Massachusetts Institute of Technology, 2005.
  8. J. Lindamood, R. Heatherly, M. Kantarcioglu, and B. Thuraisingham. Inferring private information using social network data. In WWW Poster, 2009.
  9. K. Liu and E. Terzi. Towards identity anonymization on graphs. In SIG- MOD ‟08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 93–106, New York, NY, USA, 2008. ACM.
  10. S. A. Macskassy and F. Provost. Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research, 8:935– 983, 2007.
  11. P. Sen and L. Getoor. Link-based classification. Technical Report CS-TR- 4858, University of Maryland, February 2007.
  12. L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, (5):557– 570, 2002.
  13. B. Tasker, P. Abbeel, and K. Daphne. Discriminative probabilistic models for relational data. In Proceedings of the 18th Annual Conference on Un-certainty in Artificial Intelligence (UAI-02), pages 485–492, San Francisco, CA, 2002. Morgan Kaufmann Publishers.
  14. C. van Rijsbergen, S. Robertson, and M. Porter. New models in probabilistic information retrieval. Technical Report 5587, British Library, 1980.
  15. D. J. Watts and S. H. Strogatz. Collective dynamics of small-world networks Nature, 393(6684):440–442, June 4 1998.
  16. J. Yedidia, W. Freeman, and Y. Weiss. Exploring Artificial Intelligence in the New Millennium. Science & Technology Books, 2003.
  17. E. Zheleva and L. Getoor. Preserving the privacy of sensitive relationships in graph data. pages 153–171. 2008.
  18. E. Zheleva and L. Getoor. To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In WWW,2009.

Publication Details

Published in : Volume 1 | Issue 6 | November-December 2015
Date of Publication : 2015-12-25
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 227-238
Manuscript Number : IJSRSET151631
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Syeda Meraj Bilfaqih, " Preventing Private Information Inference Attacks on Social Networks, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 1, Issue 6, pp.227-238, November-December-2015.
Journal URL :

Article Preview