Privacy Preserving Data Publishing via Weighted Bayesian Networks

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.