Centralized privacy protection method for decision model release

A privacy protection and decision-making model technology, applied in the field of privacy protection, can solve the problems of reduced accuracy of decision-making models, leakage of personal privacy and other issues, and achieve the effect of ensuring accuracy, simple process and good practicability
CN113204782APending Publication Date: 2021-08-03XIAN UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
XIAN UNIV OF POSTS & TELECOMM
Publication Date
2021-08-03

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Abstract

The invention discloses a centralized privacy protection method for decision model release, and aims to overcome the defects of the existing privacy protection technology for issuing the decision model. According to the method, firstly, decision model construction and privacy modeling are carried out respectively according to an intelligent decision scene data model and privacy protection requirements, then, sensitivity is calculated based on a privacy model, finally, Laplace noise is added to model parameters of the decision model to realize privacy protection, and the decision model which can be directly published is obtained. Compared with a common privacy protection method oriented to simple data statistics, the invention has the advantages that privacy modeling is carried out according to the data model in the decision-making scene, the defect of lack of privacy protection normal forms is made up theoretically, privacy protection is achieved based on Laplace noise disturbance, and meanwhile the accuracy of the decision-making model is guaranteed.
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Description

Technical field

[0001] The present invention relates to a privacy protection method, which specifically relates to a centralized privacy protection method for decision model issuance. Background technique

[0002] With the development and popularity of group-based sensation applications based on artificial intelligence technology, intelligent decision models have gradually played an important role in people's daily work and life. Therefore, more and more users are involved in the various group sensation applications, share and contribute their own perceived data. Third-party service providers collect and analyze a large number of user perceived data to provide users with corresponding intelligent decisions or service recommendations. However, when using a smart decision model based on perceived data learning, it will bring unprecedented privacy leaks to participate in users. For example, an attacker can use a decision model for reverse attack, infer information such as user recor...

Claims

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