Personalized recommendation method and system satisfying user privacy protection based on association rules

Abstract

The invention discloses an individuality recommendation method and system protecting user privacy on the basis of association rules. According to the method, the dimension agreement stipulation technology is used for obtaining an agreement stipulation expression of original data, and a Laplace mechanism or an index mechanism is adopted for ensuring that the agreement stipulation process meets epsilon1-differential privacy; a mining technology of a closed frequency mode is used for constructing prefix trees corresponding to agreement stipulation data, and the Laplace mechanism is utilized for disturbing the support degree count corresponding to the frequency mode to ensure that the support degree count meets epsilon2-differential privacy; meanwhile, availability of output results can be ensured by utilizing consistence constraint post-processing; the prefix trees are mined to obtain a frequency mode set meeting the epsilon-differential privacy and the support degree count corresponding to the frequency mode set; an association rule finding algorithm is used for obtaining a strong association rule set meeting the minimum support degree, the minimum confidence degree and the epsilon-differential privacy. The method effectively solves the problem of contradiction between protection of the user privacy and promotion of the performance of the individuality recommendation system and can be widely applied to the individuality recommendation systems of E-commerce, social networking, advertising and the like.

Description

technical field [0001] The invention belongs to the fields of information technology and computer technology, and relates to a data mining method, in particular to an association rule mining method under differential privacy, and adopts the method to realize a personalized recommendation system and ensure the protection of user privacy. Background technique [0002] The personalized recommendation system is an advanced intelligent platform based on massive data mining, which can recommend information and products of interest to users according to their interests and operating behaviors. Taking e-commerce as an example, e-commerce websites (such as Amazon, Taobao, etc.) recommend products for users, and automatically complete the process of personalized product selection to meet the individual needs of users. Among them, the personalized recommendation system based on association rules is based on association rules, with the purchased product as the rule head and the rule bod...

AI Technical Summary

Problems solved by technology

In addition, traditional privacy protection models cannot quantitatively analyze the level of privacy protection

[0004] Differential privacy, as a new privacy protection model, can solve two major defects of the traditional privacy protection model: (1) It defines a fairly strict attack model, which does not care how much background knowledge the attacker has, even if the attacker has mastered some For all record information other than one record, the private information of this record cannot be disclosed; (2) Provides a rigorous definition and quantitative evaluation method for the level of privacy protection

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