This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Preventing Private Information Inference Attacks on Social Networks
Aug. 2013 (vol. 25 no. 8)
pp. 1849-1862
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.
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 and Data Engineering, vol. 25, no. 8, pp. 1849-1862, Aug. 2013, doi:10.1109/TKDE.2012.120
Usage of this product signifies your acceptance of the Terms of Use.