Association data release with the randomised response based on Bayesian networks. (22nd October 2019)

• Record Type: Journal Article
• Authors: Yang, Gaoming; Dong, Tao; Fang, Xianjin; Su, Shuzhi
• Is Part Of: International journal of computational science and engineering. Volume 20:Number 1(2019)
• Page Start: 120
• Page End: 129
• Publication Date: 2019-10-22
• Subjects: association data -- local differential privacy -- Bayesian network -- privacy preserving; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.