Research on differential privacy protection parameter configuration method based on confidence level

Abstract

In order to solve the problem that the user's real data information is disclosed during the data release and analysis process, and reduce the probability of an attacker gaining real results through differential attacks and probabilistic inference attacks, a differential privacy protection parameter configuration method based on confidence level is proposed. Analysis of attacker confidence under attacker probabilistic inference attack model and make it no higher than the privacy probability threshold set according to the data privacy attribute. The proposed method can configure more reasonable privacy protection parameters for different query privilege of query users, and avoids the risk of privacy disclosure. The experimental analysis shows that the proposed method analyzes the correspondence between attacker confidence level and privacy protection parameters based on query privilege, noise distribution characteristics and data privacy attributes, and derives the configuration formula of privacy protection parameters, which configure the appropriate parameters without violating the privacy protection probability threshold.

Keywords

• differential privacy
• confidence level
• probability inference attack model
• privacy protection