A k-nearest neighbor query method based on trust incentive and location privacy protection

Abstract

The invention is applicable to the technical field of privacy protection, and provides a k-nearest neighbor query method based on trust incentive and location privacy protection. The user is put into the AgentID set; N users with high reputation located in the selection area CR are selected from the AgentID set as proxy users; each proxy user forwards the query request to the LBS server, receives the candidate results returned by the LBS server, and forwards it to the query user ; The query user selects at least k neighboring query results from the candidate results, and only neighbor users whose reputation value is higher than the defined threshold can cooperate with the query user to build an anonymous group, reducing the probability of malicious users in the anonymous group; Select a proxy user in the defined area to send a query request to the LBS server to prevent malicious users in the network from obtaining user information through improper means and protect the real location information of the query user.

Description

technical field [0001] The invention belongs to the technical field of privacy protection, and provides a k-nearest neighbor query method based on trust incentive and location privacy protection. Background technique [0002] With the rapid development of mobile networks and smart devices, Location Based Service (LBS) has become more and more popular, and users can find various LBS applications from software stores, such as Sifang.com, Google Maps, Baidu map etc. Through these location-based services, users can find the nearest movie theater, nearby restaurants, etc., can check traffic conditions or share places with friends, which brings great convenience to users' daily life. In order to meet the needs of users, it is often necessary to mine useful information from massive data, so researchers have designed many query methods, among which the k-nearest neighbor query method is the most widely used. However, if the user wants to obtain a more accurate query result through...

AI Technical Summary

Problems solved by technology

Li et al. considered whether other users are willing to provide help to the requester, introduced the trust incentive mechanism into k-anonymity, set up credit certificates, confirmed the identity of the other party according to the signature set by each, and passed multiple authentications between each other. To achieve privacy protection, if the authentication is successful, the reputation will be calculated according to the ratio of the service providing time to the service request time. Only when the reputation value reaches the set threshold, can you get help from other neighbors, so as to motivate users to actively respond to other neighbors. User's request, but the introduction of semi-trusted third-party servers may be a security risk

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