The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.08 - Aug. (2013 vol.25)
pp: 1849-1862
Raymond Heatherly , Vanderbilt University, Nashville
Murat Kantarcioglu , University of Texas at Dallas , Richardson
Bhavani Thuraisingham , University of Texas at Dallas, Richardson
ABSTRACT
Online social networks, such as Facebook, are increasingly utilized by many people. These networks allow users to publish details about themselves and to connect to their friends. Some of the information revealed inside these networks is meant to be private. Yet it is possible to use learning algorithms on released data to predict private information. In this paper, we explore how to launch inference attacks using released social networking data to predict private information. We then devise three possible sanitization techniques that could be used in various situations. Then, we explore the effectiveness of these techniques and attempt to use methods of collective inference to discover sensitive attributes of the data set. We show that we can decrease the effectiveness of both local and relational classification algorithms by using the sanitization methods we described.
INDEX TERMS
Privacy, Facebook, Data privacy, Inference algorithms, Knowledge engineering, Equations, social network privacy, Social network analysis, data mining
CITATION
Raymond Heatherly, Murat Kantarcioglu, Bhavani Thuraisingham, "Preventing Private Information Inference Attacks on Social Networks", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 8, pp. 1849-1862, Aug. 2013, doi:10.1109/TKDE.2012.120
REFERENCES
[1] Facebook Beacon, 2007.
[2] T. Zeller, "AOL Executive Quits After Posting of Search Data," The New York Times, no. 22, http://www.nytimes.com/2006/08/22/technology 22iht-aol.2558731.html?pagewanted=all&_r=0 , Aug. 2006.
[3] K.M. Heussner, "'Gaydar' n Facebook: Can Your Friends Reveal Sexual Orientation?" ABC News, http://abcnews.go. com/Technology/gaydar-facebook-friends story?id=8633224#. UZ939UqheOs , Sept. 2009.
[4] C. Johnson, "Project Gaydar," The Boston Globe, Sept. 2009.
[5] L. Backstrom, C. Dwork, and J. Kleinberg, "Wherefore Art Thou r3579x?: Anonymized Social Networks, Hidden Patterns, and Structural Steganography," Proc. 16th Int'l Conf. World Wide Web (WWW '07), pp. 181-190, 2007.
[6] M. Hay, G. Miklau, D. Jensen, P. Weis, and S. Srivastava, "Anonymizing Social Networks," Technical Report 07-19, Univ. of Massachusetts Amherst, 2007.
[7] K. Liu and E. Terzi, "Towards Identity Anonymization on Graphs," Proc. ACM SIGMOD Int'l Conf. Management of Data (SIGMOD '08), pp. 93-106, 2008.
[8] J. He, W. Chu, and V. Liu, "Inferring Privacy Information from Social Networks," Proc. Intelligence and Security Informatics, 2006.
[9] E. Zheleva and L. Getoor, "Preserving the Privacy of Sensitive Relationships in Graph Data," Proc. First ACM SIGKDD Int'l Conf. Privacy, Security, and Trust in KDD, pp. 153-171, 2008.
[10] R. Gross, A. Acquisti, and J.H. Heinz, "Information Revelation and Privacy in Online Social Networks," Proc. ACM Workshop Privacy in the Electronic Soc. (WPES '05), pp. 71-80, http://dx.doi.org/10.11451102199.1102214 , 2005.
[11] H. Jones and J.H. Soltren, "Facebook: Threats to Privacy," technical report, Massachusetts Inst. of Tech nology, 2005.
[12] P. Sen and L. Getoor, "Link-Based Classification," Technical Report CS-TR-4858, Univ. of Maryland, Feb. 2007.
[13] B. Tasker, P. Abbeel, and K. Daphne, "Discriminative Probabilistic Models for Relational Data," Proc. 18th Ann. Conf. Uncertainty in Artificial Intelligence (UAI '02), pp. 485-492, 2002.
[14] A. Menon and C. Elkan, "Predicting Labels for Dyadic Data," Data Mining and Knowledge Discovery, vol. 21, pp. 327-343, 2010.
[15] 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," Technical Report CS-TR-4926, Univ. of Maryland, College Park, July 2008.
[16] N. Talukder, M. Ouzzani, A.K. Elmagarmid, H. Elmeleegy, and M. Yakout, "Privometer: Privacy Protection in Social Networks," Proc. IEEE 26th Int'l Conf. Data Eng. Workshops (ICDE '10), pp. 266-269, 2010.
[17] J. Lindamood, R. Heatherly, M. Kantarcioglu, and B. Thuraisingham, "Inferring Private Information Using Social Network Data," Proc. 18th Int'l Conf. World Wide Web (WWW), 2009.
[18] S.A. Macskassy and F. Provost, "Classification in Networked Data: A Toolkit and a Univariate Case Study," J. Machine Learning Research, vol. 8, pp. 935-983, 2007.
[19] L. Sweeney, "k-Anonymity: A Model for Protecting Privacy," Int'l J. Uncertainty, Fuzziness and Knowledge-based Systems, pp. 557-570, 2002.
[20] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, "L-Diversity: Privacy Beyond K-Anonymity," ACM Trans. Knowledge Discovery from Data, vol. 1, no. 1, p. 3, 2007.
[21] C. Dwork, "Differential Privacy," Automata, Languages and Programming, M. Bugliesi, B. Preneel, V. Sassone, and I. Wegener, eds., vol. 4052, pp. 1-12, Springer, 2006.
[22] A. Friedman and A. Schuster, "Data Mining with Differential Privacy," Proc. 16th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, pp. 493-502, 2010.
[23] K. Fukunaga and D.M. Hummels, "Bayes Error Estimation Using Parzen and K-nn Procedures," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 5, pp. 634-643, http://portal.acm.orgcitation.cfm?id=28809.28814 , Sept. 1987.
[24] C. Clifton, "Using Sample Size to Limit Exposure to Data Mining," J. Computer Security, vol. 8, pp. 281-307, http://portal.acm.orgcitation.cfm?id=371090.371092 , Dec. 2000.
[25] K. Tumer and J. Ghosh, "Bayes Error Rate Estimation Using Classifier Ensembles," Int'l J. Smart Eng. System Design, vol. 5, no. 2, pp. 95-110, 2003.
[26] C. van Rijsbergen, S. Robertson, and M. Porter, "New Models in Probabilistic Information Retrieval," Technical Report 5587, British Library, 1980.
[27] D.J. Watts and S.H. Strogatz, "Collective Dynamics of Small-World Networks," Nature, vol. 393, no. 6684, pp. 440-442, June 1998.
35 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool