Journal
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
Volume 18, Issue 1, Pages 9-32Publisher
SPRINGER
DOI: 10.1007/s11280-013-0240-6
Keywords
Social network analysis; Privacy-preserving data publishing; Link perturbation
Funding
- Natural Sciences and Engineering Research Council of Canada
- Chinese National 111 Project Attracting International Talents in Data Engineering and Knowledge Engineering Research
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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.
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