AKIEF: Adaptive Knowledge Inheritance Evolutionary Framework for Dynamic Privacy-Preserving Data Publishing

Privacy-Preserving Data Publishing (PPDP) faces significant challenges in dynamic scenarios where evolving datasets require continuous anonymization while maintaining data utility and resisting temporal attacks. This paper introduces an Adaptive Knowledge Inheritance Evolutionary Framework (AKIEF), a novel approach employing an Adaptive Differential Evolution (ADE) strategy tailored for dynamic PPDP under l -diversity constraints. AKIEF integrates several key innovations to enhance optimization efficiency and effectiveness: (1) a Depth-First Search (DFS) combined initialization strategy to improve the quality of the starting population at the initial time step ( t = 1); (2) a knowledge inheritance mechanism that leverages elite solutions from previous time windows to accelerate adaptation and convergence in subsequent steps ( t > 1); and (3) a novel two-dimension adaptive mutation strategy that dynamically and independently adjusts exploration-exploitation balance for generalization and suppression components within the ADE process. Comprehensive experiments are conducted using 16 test configurations derived from a large, real-world healthcare dataset, comparing AKIEF against baseline methods and static state-of-the-art algorithms. The results demonstrate the AKIEF frequently achieves superior data utility, exhibits robustness by consistently finding high-quality solutions where others struggle, and significantly accelerates convergence in dynamic environments. These findings demonstrate the advantages of the AKIEF as an effective and robust solution for the demanding challenges of dynamic PPDP.