Federated Genetic Algorithm: Two-Layer Privacy-Preserving Trajectory Data Publishing
Yong-Feng Ge, Hua Wang, Jinli Cao, Yanchun Zhang, Georgios Kambourakis · Proceedings of the Genetic and Evolutionary Computation Conference · 2024
Nowadays, trajectory data is widely available and used in various real-world applications such as urban planning, navigation services, and location-based services. However, publishing trajectory data can potentially leak sensitive information about identity, personal profiles, and social relationships, and requires privacy protection. This paper focuses on optimizing Privacy-Preserving Trajectory Data Publishing (PP-TDP) problems, addressing the limitations of existing techniques in the trade-off between privacy protection and information preservation. We propose the Federated Genetic Algorithm (FGA) in this paper, aiming to achieve better local privacy protection and global information preservation. FGA consists of multiple local optimizers and a single global optimizer. The parallel local optimizer enables the local data center to retain the original trajectory data and share only the locally anonymized outcomes. The global optimizer collects the local anonymized outcomes and further optimizes the preservation of information while achieving comprehensive privacy protection. To optimize the discrete-domain PP-TDP problems more efficiently, this paper proposes a grouping-based strategy, an intersection-based crossover operation, and a complement-based mutation operation. Experimental results demonstrate that FGA outperforms its competitors in terms of solution accuracy and search efficiency.