Demand privacy protection method based on differential privacy and association rules

Abstract

The invention provides a demand privacy protection method based on differential privacy and association rules. The method can effectively protect the privacy of the query content of a user in a socialnetwork, and Laplace noise (the differential privacy) is added to the confidence among required things, so that the privacy of the user is further protected. By adopting the privacy protection methodof the game, not only can the privacy required by the user be protected, but also the service quality of the user can be well guaranteed, and a privacy protection system can adaptively protect the privacy of the user. For the privacy preference of the user, the dynamic protection method is adopted to meet different privacy requirements of the user.

Description

technical field [0001] The invention relates to the field of computer information technology, in particular to a demand privacy protection method based on differential privacy and association rules. Background technique [0002] At present, the existing technologies basically protect the user's location privacy. There are four broad categories of location privacy protection technologies, including spatial blurring, virtual objects, encryption technology, and differential privacy protection. Some scholars add noise to people's real location to protect people's location privacy, and attackers generally get the location information after "noise addition". The most typical application case of spatial fuzzy is K-anonymity. In 2003, Gruteser et al. first applied the K-anonymity method to protect the real location of users. In a certain time and space, each user needs to be indistinguishable from other K-1 users, so the attacker cannot distinguish the target user and infer its loc...

AI Technical Summary

Problems solved by technology

In 2003, Gruteser et al. first applied the K-anonymity method to protect the real location of users. In a certain time and space, each user needs to be indistinguishable from other K-1 users, so the attacker cannot distinguish the target user and infer its location

[0003] At present, there is no special demand privacy method to protect people's demand privacy issues, which makes people's demand privacy seriously leaked, which has a serious impact on people's lives

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