research-article

Distributed Cooperative Coevolution of Data Publishing Privacy and Transparency

Authors:

Yanchun ZhangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 1

Article No.: 20, Pages 1 - 23

https://doi.org/10.1145/3613962

Published: 06 September 2023 Publication History

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.

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 1

January 2024

854 pages

EISSN:1556-472X

DOI:10.1145/3613504

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 06 September 2023

Online AM: 07 August 2023

Accepted: 28 July 2023

Revised: 28 June 2023

Received: 08 March 2023

Published in TKDD Volume 18, Issue 1

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