Abstract
Budget factor is an important factor to measure the intensity of differential privacy, and its allocation scheme has a great impact on privacy protection. This paper studies the selection of the parameter ε in several cases of differential privacy. Firstly, this paper proposes a differential privacy protection parameter configuration method based on fault tolerance interval and analyzes the adversaryʼs fault tolerance under different noise distribution location parameters and scale parameters. Secondly, this paper proposes an algorithm to optimize the application scenarios of multiquery, studies the location parameters and scale parameters in detail, and proposes a differential privacy mechanism to solve the multiuser query scenarios. Thirdly, this paper proposes the differential privacy parameter selection methods based on the single attack and repeated attacks and calculates the upper bound of the parameter ε based on the sensitivity Δ q , the length of the fault tolerance interval L , and the success probability p as long as the fault tolerance interval. Finally, we have carried out a variety of simulation experiments to verify our research scheme and give the corresponding analysis results.
Highlights
In recent years, with the rapid development of information technology, user data have experienced explosive growth
Aiming at the above problems, to ensure the privacy and availability of sensitive data in the process of data query, solve the problem of real data information leakage in the process of data, and reduce the probability of attackers to obtain real results through differential attack and probabilistic reasoning attack, we study the differential privacy parameter selection methods in various situations; these specific contributions are as follows: (i) We propose a differential privacy parameter configuration method based on fault tolerance interval and analyze the adversarys fault tolerance under different noise distribution location parameters and scale parameters and study the influence of the user’s query permission on privacy protection parameter configuration
(ii) We study the location parameters and scale parameters in detail and propose a differential privacy mechanism to solve the multi-user query scenarios
Summary
With the rapid development of information technology, user data have experienced explosive growth. Most of the proposed privacy protection schemes use anonymous fuzzy or data distortion processing (such as adding random noise) and other technologies and use mathematical regression analysis, data distortion adjustment, and noise scale parameter adjustment to reduce the error caused by noise, so as to improve the availability of data [12,13,14] These schemes have some shortcomings; that is, the same query results will cause the disclosure of privacy information when the query users with. Aiming at the above problems, to ensure the privacy and availability of sensitive data in the process of data query, solve the problem of real data information leakage in the process of data, and reduce the probability of attackers to obtain real results through differential attack and probabilistic reasoning attack, we study the differential privacy parameter selection methods in various situations; these specific contributions are as follows:.
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