Anonymizing Social Graphs via Uncertainty Semantics

TL;DR

This paper introduces a new anonymization approach for social graphs using uncertain adjacency matrices, maintaining node degrees and improving privacy while enabling fair comparison of different schemes.

Contribution

It proposes a generalized obfuscation model based on uncertain adjacency matrices, analyzes existing schemes within this framework, and develops a new tradeoff quantification method.

Findings

Effective anonymization of large social graphs demonstrated

Identified disadvantages in existing schemes and proposed improvements

Framework enables fair comparison of privacy-preserving methods

Abstract

Rather than anonymizing social graphs by generalizing them to super nodes/edges or adding/removing nodes and edges to satisfy given privacy parameters, recent methods exploit the semantics of uncertain graphs to achieve privacy protection of participating entities and their relationship. These techniques anonymize a deterministic graph by converting it into an uncertain form. In this paper, we propose a generalized obfuscation model based on uncertain adjacency matrices that keep expected node degrees equal to those in the unanonymized graph. We analyze two recently proposed schemes and show their fitting into the model. We also point out disadvantages in each method and present several elegant techniques to fill the gap between them. Finally, to support fair comparisons, we develop a new tradeoff quantifying framework by leveraging the concept of incorrectness in location privacy research. Experiments on large social graphs demonstrate the effectiveness of our schemes.