Issue No. 08 - Aug. (2013 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.120
Raymond Heatherly , Vanderbilt University, Nashville
Murat Kantarcioglu , University of Texas at Dallas , Richardson
Bhavani Thuraisingham , University of Texas at Dallas, Richardson
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.
Privacy, Facebook, Data privacy, Inference algorithms, Knowledge engineering, Equations, social network privacy, Social network analysis, data mining
R. Heatherly, M. Kantarcioglu and B. Thuraisingham, "Preventing Private Information Inference Attacks on Social Networks," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 1849-1862, 2013.