Fuzzy k-anonymity in complex networks

TL;DR

This paper introduces a fuzzy variant of k-anonymity called φ-k-anonymity for complex networks, accounting for attacker knowledge uncertainty, and demonstrates its effectiveness in improving privacy while preserving data utility.

Contribution

The paper proposes φ-k-anonymity, a new privacy model that incorporates attacker uncertainty, and develops algorithms that significantly improve anonymity in real-world and synthetic networks.

Findings

Modest uncertainty levels anonymize 64% of nodes on average.

Greedy algorithm anonymizes over 99% of nodes with 5% edge modifications.

Structural properties are preserved with less than 5% change in most metrics.

Abstract

With the introduction of large-scale network data, including population-scale social networks, techniques for privacy-aware sharing of network data become increasingly important. While existing $k$-anonymity approaches can model different attacker scenarios, they typically assume that attacker knowledge exactly matches the published network structure. We argue that exact knowledge is often unrealistic and introduce $\phi$-$k$-anonymity, a fuzzy variant of $k$-anonymity in which parameter $\phi$ captures the level of uncertainty in attacker knowledge. Across a benchmark of $39$ real-world networks, a realistic level of uncertainty ($\phi=5\%$) renders, on average, $64\%$ of previously unique nodes anonymous. To further enhance anonymity, we apply anonymization algorithms under a $5\%$ edge modification budget. While full anonymization is often unattainable under exact $k$-anonymity, with low uncertainty ($\phi=10\%$) our newly proposed Greedy algorithm anonymizes over $99\%$ of the nodes. Uncertainty also enables effective anonymization in otherwise difficult to anonymize dense synthetic graphs. Additionally, data utility in terms of structural properties and performance on network analysis tasks is well preserved, with most metrics changing less than $5\%$. Overall, our findings suggest that modest uncertainty assumptions yield high levels of anonymity and utility, motivating further research on uncertainty-aware privacy guarantees for network data.