LBS service privacy protection method based on differential privacy

Abstract

The invention discloses an LBS service privacy protection method based on differential privacy. A query k-anonymity set conforming to differential privacy is constructed according to background knowledge of a cluster where a user is located and an area where the user is located obtained in a DP-(k1, l)-means algorithm, and the anonymity set is sent to an LSP, so that leakage of a real query request of a user is avoided, the query privacy of the user is protected, and reasoning attacks and space-time correlation attacks of attackers are avoided. In a DP-k2-anonymy algorithm, according to the time characteristics, query requests sent in the same time period t are selected to construct a query k-anonymity set, and the data set is processed by using an index mechanism, so that the reasonability of the query requests in time is ensured, and the privacy of users is protected from being leaked.

Description

technical field [0001] The present invention relates to a privacy protection method, and more specifically, the present invention relates to a differential privacy-based LBS service privacy protection method. Background technique [0002] With the popularization of mobile networks and the development of positioning technology, location-based service (Location-Based Service, LBS) has been recognized by a large number of users. These service systems greatly facilitate people's lives, but they often collect request data sent by users without the user's knowledge, and analyze and process these data, causing problems such as leakage of user's private information. LBS-based service requests contain a large amount of private information, such as user identity information, location information, point-of-interest information, etc., and these information may be leaked while sending out the query request. Therefore, how to protect the user's private information without affecting the u...

AI Technical Summary

Problems solved by technology

The Dummy-Q model proposed by Pinglery et al. constructs fake queries based on relevant conditions such as query context, user motion model, and query semantics, so that attackers cannot distinguish the difference between real queries and fake queries.

In the above query privacy protection algorithm, the real query request of the user is not combined. If the real query request of the user is not included in the POI with a high unit query probability, the returned query result is useless to the user.

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