Link Privacy in Social Networks

We consider a privacy threat to a social network in which the goal of an attacker is to obtain knowledge of a significant fraction of the links in the network. We formalize the typical social network interface and the information about links that it provides to its users in terms of lookahead. We consider a particular threat in which an attacker subverts user accounts to gain information about local neighborhoods in the network and pieces them together in order to build a global picture. We analyze, both experimentally and theoretically, the number of user accounts an attacker would need to subvert for a successful attack, as a function of his strategy for choosing users whose accounts to subvert and a function of the lookahead provided by the network. We conclude that such an attack is feasible in practice, and thus any social network that wishes to protect the link privacy of its users should take great care in choosing the lookahead of its interface, limiting it to 1 or 2, whenever possible.

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

[2]  Gábor Csányi,et al.  Structure of a large social network. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Brendan D. McKay,et al.  The Asymptotic Number of Labeled Graphs withnVertices, qEdges, and No Isolated Vertices , 1997, J. Comb. Theory, Ser. A.

[4]  Amin Saberi,et al.  Random Walks with Lookahead on Power Law Random Graphs , 2006, Internet Math..

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

[6]  Fan Chung Graham,et al.  A Random Graph Model for Power Law Graphs , 2001, Exp. Math..

[7]  Rajeev Motwani,et al.  Link Privacy in Social Networks , 2008, ICDE.

[8]  Alan M. Frieze,et al.  A general model of web graphs , 2003, Random Struct. Algorithms.

[9]  Edward A. Bender,et al.  The Asymptotic Number of Labeled Graphs with Given Degree Sequences , 1978, J. Comb. Theory, Ser. A.

[10]  John Scott What is social network analysis , 2010 .

[11]  Jon M. Kleinberg,et al.  Navigation in a small world , 2000, Nature.

[12]  Eli Upfal,et al.  Stochastic models for the Web graph , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[13]  Hillol Kargupta,et al.  Privacy-Preserving Data Analysis on Graphs and Social Networks , 2008, Next Generation of Data Mining.

[14]  Lada A. Adamic,et al.  Search in Power-Law Networks , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Christos Faloutsos,et al.  Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication , 2005, PKDD.

[16]  Lise Getoor,et al.  Preserving the Privacy of Sensitive Relationships in Graph Data , 2007, PinKDD.

[17]  H. V. Schelling Coupon Collecting for Unequal Probabilities , 1954 .

[18]  坂倉 省吾,et al.  Technology Review : 抄録雑誌の概要 , 1987 .

[19]  Béla Bollobás,et al.  Directed scale-free graphs , 2003, SODA '03.

[20]  Karen D Kirke [Cambridge University Press Prize for Best Student Paper] When There's More than One Norm-Enforcement Mechanism: Accommodation and Shift among Irish Immigrants to New York City , 2005 .

[21]  Christos Gkantsidis,et al.  Conductance and congestion in power law graphs , 2003, SIGMETRICS '03.

[22]  Stanley Wasserman,et al.  Wasserman, Stanley, and Katherine Faust, Social Network Analysis: Methods and Applications. New York: Cambridge University Press, 1994. , 1994 .

[23]  Siddharth Srivastava,et al.  Anonymizing Social Networks , 2007 .

[24]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

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