Fuzzy k-anonymity in complex networks

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.