De-anonymization of Social Networks with Communities: When Quantifications Meet Algorithms

TL;DR

This paper introduces new cost functions for social network de-anonymization leveraging community structures, providing theoretical guarantees and algorithms that outperform previous methods, validated on real cross-domain social network data.

Contribution

It proposes well-justified cost functions based on MAP estimation, characterizes their algorithmic feasibility, and develops algorithms with approximation guarantees for de-anonymization.

Findings

Cost functions outperform previous models in identifying correct mappings.

Algorithms achieve psilon-additive approximation and conditional optimality.

Community information significantly enhances privacy inference capabilities.

Abstract

A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated cross-domain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through cost functions that are proposed without sufficient rationale. Also, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to find the minimizer of the cost functions. We address those concerns in a more realistic social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. By Maximum A Posteriori (MAP) estimation, our first contribution is new and well justified cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized. While proving the general multiplicative inapproximability, we are able to propose two algorithms, which, respectively, enjoy an \epsilon-additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are empirically validated, with a notable dataset extracted from rare true cross-domain networks that reproduce genuine social network de-anonymization. Both theoretical and empirical observations also manifest the importance of community information in enhancing privacy inferencing.