Fuzzy k-anonymity in complex networks

Scientific Reports, vol. 16

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 ϕ-k-anonymity, a fuzzy variant of k-anonymity in which parameter ϕ captures the level of uncertainty in attacker knowledge. Across a benchmark of 39 real-world networks, a modest level of uncertainty ([Formula: see text]) renders, on average, [Formula: see text] 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 ([Formula: see text]) 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 metrics capturing structural properties and performance on network analysis tasks are well preserved, with five out of six 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.

Authors 3

1. R. Jong corresponding Aachen

   Leiden University · Centraal Bureau voor de Statistiek

   Affiliation as printed

   Leiden University, LIACS, 2333 CA, Leiden, The Netherlands. r.g.de.jong@liacs.leidenuniv.nl

   Statistics Netherlands, Research and Development, 2492JP, The Hague, The Netherlands. r.g.de.jong@liacs.leidenuniv.nl

2. Mark van der Loo Aachen

   Leiden University · Centraal Bureau voor de Statistiek

   Affiliation as printed

   Leiden University, LIACS, 2333 CA, Leiden, The Netherlands

   Statistics Netherlands, Research and Development, 2492JP, The Hague, The Netherlands

3. Frank W. Takes Aachen

   Leiden University

   Affiliation as printed

   Leiden University, LIACS, 2333 CA, Leiden, The Netherlands