An enhanced discrete particle swarm optimization for structural k-Anonymity in social networks

Abstract

Recently, social network usage has exhibited explosive growth, leading to a huge amount of users’ private data available. The main challenge in releasing social network data publicly is the protection of the users’ privacy while preserving its utility for third parties. Accordingly, several social network privacy-preserving methods have been introduced, where anonymization is the most common approach. Structural k-anonymity is a widely used anonymization model to mask the structure of social networks by clustering the edges and nodes into super-edges and super-nodes. However, it comes at the cost of losing structural information, which is measured by a criterion called structural information loss (SIL). This study introduces an enhanced discrete particle swarm optimization (EDPSO) algorithm, which effectively minimizes the SIL within the clustering process of the structural k-anonymity model, leading to a high-utility anonymized network. In this regard, we propose a vector-based solution representation that can be efficiently exploited by the EDPSO. Moreover, a novel position updating heuristic is suggested for the EDPSO, which adaptively tunes the operators’ selection probabilities. This happens based on each operator’s performance both in the current iteration and their history regarding the number and the average amount of fitness improvements in the previous iterations. We also propose two fortified versions of the EDPSO algorithm (EDPSOVNS and EDPSOSA) by employing two new network-specific local search strategies to enhance the exploration, exploitation, and convergence rate of the process. Simulation results on the nine real-world networks demonstrate the superiority of the suggested algorithms in terms of the fitness value, reliability, and convergence rate over other analyzed approaches found in the literature.