ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (10): 2343-2353.doi: 10.7544/issn1000-1239.2016.20160465

Special Issue: 2016网络空间共享安全研究进展专题

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Privacy Preserving Data Publishing via Weighted Bayesian Networks

Wang Liang1,2, Wang Weiping1, Meng Dan1   

  1. 1(Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093); 2(University of Chinese Academy of Sciences, Beijing 100049)
  • Online:2016-10-01

Abstract: Privacy preserving in data publishing is a hot topic in the field of information security currently. How to effectively prevent the disclosure of sensitive information has become a major issue in enabling public access to the published dataset that contain personal information. As a newly developed notion of privacy preserving, differential privacy can provide strong security protection due to its greatest advantage of not making any specific assumptions on the attacker's background, and has been extensively studied. The existing approaches of differential privacy cannot fully and effectively solve the problem of releasing high-dimensional data. Although the PrivBayes can transform high-dimensional data to low-dimensional one, but cannot prevent attributes disclosure on certain conditions, and also has some limitations and shortcomings. In this paper, to solve these problems, we propose a new and powerful improved algorithm for data publishing called weighted PrivBayes. In this new algorithm, thorough both theoretical analysis and experiment evaluation, not only guarantee the security of the published dataset but also significantly improve the data accuracy and practical value than PrivBayes.

Key words: data privacy, Bayesian network, privacy preserving, data publishing, differential privacy

CLC Number: