Distributed Bandit-Based Cooperative Coevolution for Large-Scale Multi-Objective Data Publishing

When publishing data, organizations must balance transparency requirements with data privacy concerns. This paper introduces a Distributed Bandit-based Cooperative Coevolution (DBCC) algorithm that addresses scalability and adaptability challenges in large-scale datasets. The DBCC optimizes privacy protection and transparency through a divide-and-conquer approach using three sequential modules: clustering-based grouping module, bandit-based Cooperative Coevolution (CC) module, and evolving module. In the grouping module, the DBCC algorithm clusters similar records into the same subproblems. The CC module optimizes subproblems in sub-populations simultaneously while measuring their current and potential contributions. It adaptively allocates population resources to maintain a balance between exploration and exploitation. To enable Differential Evolution (DE) in discrete-domain data publishing, the algorithm implements a set-based mutation operation and a transparency-driven crossover operation. The evolving module then generates the final complete solutions. Through our experiment, we demonstrate that the proposed DBCC achieves significantly higher solution accuracy than existing state-of-the-art algorithms across all 15 test instances, showing an improvement of 247.16%. We validate the effectiveness of all DBCC components and investigate the impact of parameters. Additionally, our investigation reveals that speedup ratios can reach up to 3.45 when parallel granularity is 10.