Distributed Cooperative Coevolution of Data Publishing Privacy and Transparency
Yong-Feng Ge, Elisa Bertino, Hua Wang, Jinli Cao, Yanchun Zhang
Abstract
Data transparency is beneficial to data participants’ awareness, users’ fairness, and research work’s reproducibility. However, when addressing transparency requirements, we cannot ignore data privacy. This article defines the multi-objective data publishing (MODP) problem, optimizing data privacy and transparency at the same time. Accordingly, we propose a distributed cooperative coevolutionary genetic algorithm (DCCGA) to optimize the MODP problem. In the population of DCCGA, each individual represents an anonymization solution to MODP. Three modules in DCCGA, i.e., grouping module, cooperative coevolutionary module, and evolving module, are proposed for distributed sub-population update and evaluation, improving DCCGA’s optimization performance and parallel efficiency. Moreover, a matrix-based crossover operator and a matrix-based mutation operator are designed to exchange and adjust anonymization information in the individuals efficiently. Experimental results demonstrate that the proposed DCCGA outperforms the competitors with respect to solution accuracy, convergence speed, and scalability. Besides, we verify the effectiveness of all the proposed components in DCCGA.