A Framework for Computing the Privacy Scores of Users in Online Social Networks

A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. The main focus was on how to share social networks owned by organizations without revealing the identities or sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their information sharing activities. In this paper, we approach the privacy concerns arising in online social networks from the individual users’ viewpoint: we propose a framework to compute a privacy score of a user, which indicates the potential privacy risk caused by his participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive the information revealed by a user, the higher his privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.

[1]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[2]  Cynthia Dwork,et al.  Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography , 2007, WWW '07.

[3]  Kristen LeFevre,et al.  Privacy wizards for social networking sites , 2010, WWW '10.

[4]  Melvin R. Novick,et al.  Some latent train models and their use in inferring an examinee's ability , 1966 .

[5]  Donald F. Towsley,et al.  Resisting structural re-identification in anonymized social networks , 2008, The VLDB Journal.

[6]  Jiawei Han,et al.  ACM Transactions on Knowledge Discovery from Data: Introduction , 2007 .

[7]  Xiaowei Ying,et al.  Randomizing Social Networks: a Spectrum Preserving Approach , 2008, SDM.

[8]  Alessandro Acquisti,et al.  Information revelation and privacy in online social networks , 2005, WPES '05.

[9]  Donald F. Towsley,et al.  Resisting structural re-identification in anonymized social networks , 2010, The VLDB Journal.

[10]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[11]  Azadeh Iranmehr,et al.  Trust Management for Semantic Web , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[12]  Jian Pei,et al.  Preserving Privacy in Social Networks Against Neighborhood Attacks , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[13]  K. Liu,et al.  Towards identity anonymization on graphs , 2008, SIGMOD Conference.

[14]  Mark D. Reckase,et al.  Item Response Theory: Parameter Estimation Techniques , 1998 .

[15]  Tjalling J. Ypma,et al.  Historical Development of the Newton-Raphson Method , 1995, SIAM Rev..

[16]  Robert J. Mislevy,et al.  BILOG 3 : item analysis and test scoring with binary logistic models , 1990 .

[17]  Frank B. Baker,et al.  Item Response Theory : Parameter Estimation Techniques, Second Edition , 2004 .

[18]  Hugo Liu.  InterestMap : Harvesting Social Network Profiles for Recommendations , 2004 .