De-anonymization of Social Networks with Communities: When Quantifications Meet Algorithms
Luoyi Fu, Xinzhe Fu, Zhongzhao Hu, Zhiying Xu, Xinbing Wang

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Internet Traffic Analysis and Secure E-voting
