The effect of constraints on information loss and risk for clustering and modification based graph anonymization methods

TL;DR

This paper introduces a new constrained graph anonymization method for social networks that reduces information loss while maintaining privacy, using novel local similarity constraints and metrics.

Contribution

It proposes a novel constrained approach for graph anonymization that improves information preservation and privacy balance over existing methods.

Findings

Constrained methods outperform unconstrained ones in information loss metrics.

The approach effectively balances privacy risk and data utility.

Empirical results on real datasets validate the method's effectiveness.

Abstract

In this paper we present a novel approach for anonymizing Online Social Network graphs which can be used in conjunction with existing perturbation approaches such as clustering and modification. The main insight of this paper is that by imposing additional constraints on which nodes can be selected we can reduce the information loss with respect to key structural metrics, while maintaining an acceptable risk. We present and evaluate two constraints, 'local1' and 'local2' which select the most similar subgraphs within the same community while excluding some key structural nodes. To this end, we introduce a novel distance metric based on local subgraph characteristics and which is calibrated using an isomorphism matcher. Empirical testing is conducted with three real OSN datasets, six information loss measures, five adversary queries as risk measures, and different levels of k-anonymity. The results show that overall, the methods with constraints give the best results for information loss and risk of disclosure.