Chinese Journal of Network and Information Security ›› 2019, Vol. 5 ›› Issue (4): 29-39.doi: 10.11959/j.issn.2096-109x.2019036

• Special Column:Researches on Key Technologies of Privacy Protection and Its Innovative Applications • Previous Articles     Next Articles

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

Senyou LI(),Xinsheng JI,Wei YOU   

  1. National Digital Switching System Engineering &Technological Research Center,Zhengzhou 450002,China
  • Revised:2019-06-06 Online:2019-08-15 Published:2019-08-20
  • Supported by:
    The National Natural Science Foundation for Creative Research Groups of China(61521003);The National Natural Science Foundation for Creative Research Groups of China(61801515);The National Key R&D Program of China(2016YFB0801605)

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.

Key words: differential privacy, confidence level, probability inference attack model, privacy protection

CLC Number: 

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