Bayesian networks-based data publishing method using smooth sensitivity

The issue of privacy protection in data publishing is an important topic in the field of information security. How to prevent the disclosure of sensitive information has become a hot topic of research. Due to the large data volume and the high correlation, high-dimensional data leads to poor data utility when data is published by differential privacy. Although the current high-dimensional data publishing methods can solve the problem of high-dimensional data publishing, the published synthetic data is of poor utility. In order to improve the utility of the published synthetic data, this paper proposes a Bayesian network-based data publishing method, which makes use of the concept of smooth sensitivity, to analyze the actual data set, which can reduce the added noise while achieving differential privacy and improve the utility of published data. The experiments are performed on real datasets and compared with the PrivBayes method to verify that the proposed method has advantages in data utility.