World Wide Web

, Volume 18, Issue 1, pp 9–32 | Cite as

Neighborhood randomization for link privacy in social network analysis

  • Amin Milani FardEmail author
  • Ke Wang


Social network analysis has many important applications but it depends on sharing and publishing the underlying graph. Link privacy requires limiting the ability of an adversary to infer the presence of a sensitive link between two individuals in the published social network graph. A standard technique for achieving link privacy is to probabilistically randomize a link over the space for node pairs. A major drawback of such graph-wise randomization is that it ignores the structural proximity of nodes, thus, alters considerably the structure of social networks and distorts the accuracy of social network analysis. To address this problem, we propose a structure-aware randomization scheme, called neighborhood randomization. This scheme models a social network as a directed graph and probabilistically randomizes the destination of a link within a local neighborhood. By confining the randomization to a local neighborhood, this scheme drastically reduces the distortion to the graph structure yet hides a sensitive link. The trade-off between privacy and utility is dictated by the retention probability of a destination and by the size of the randomization neighborhood. We conduct extensive experiments to evaluate this trade-off using real life social network data.


Social network analysis Privacy-preserving data publishing Link perturbation 


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  2. 2.University of British ColumbiaVancouverCanada

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