Title: Association data release with the randomised response based on Bayesian networks

Authors: Gaoming Yang; Tao Dong; Xianjin Fang; Shuzhi Su

Addresses: School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China ' School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, China

Abstract: Local differential privacy is one of the most effective methods for privacy protection data publishing, whose theoretical basis is the randomised response. However, the existing models assume that data attributes are independent of each other, which might result in excessive information loss. To solve this issue, we present a local differentially private method for releasing association data. First, to find the relationship between attributes, we constructed a Bayesian network with a greedy algorithm base on mutual information for the given dataset. Second, to ensure local differential privacy, we perturbed each dependent attribute pair according to weak or robust association attribute set. Third, to achieve the local differential privacy with the noisy marginal, we constructed an approximation distribution for the given dataset. Finally, we experimentally evaluated our method on real data, and the extensive results show that our method better balances data utility and privacy disclosure.

Keywords: association data; local differential privacy; Bayesian network; privacy preserving.

DOI: 10.1504/IJCSE.2019.10024813

International Journal of Computational Science and Engineering, 2019 Vol.20 No.1, pp.120 - 129

Received: 03 Jan 2019
Accepted: 06 May 2019
Published online: 23 Oct 2019 *

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