K-anonymity-based personalized position privacy protection method

Abstract

The invention discloses a K-anonymity-based personalized position privacy protection method, and belongs to the field of information security. The method includes: generating an efficient k- anonymousset by a personalized false position generation algorithm; using the anonymous set to replace a real position and sending the anonymous set to a service provider, so that the position privacy of theuser is protected when the user sends a request based on the position service (LBS), and the user can define the anonymous degree and the service demand according to the actual condition to obtain a query result meeting the privacy protection demand. The method is suitable for services of a single discrete request, such as interest point retrieval.

Description

technical field [0001] The invention relates to a personalized fake location generation algorithm, which belongs to the field of information security, in particular to a k-anonymity-based location privacy protection method. Background technique [0002] With the rapid development of wireless communication networks and mobile devices, location-based services (LBS) have become an indispensable service in daily life. LBS refers to obtaining corresponding requested services from an LBS provider based on the geographical location of a mobile user. When users obtain services, they need to provide their own location information. From the location information, other sensitive information such as the user's home address and living habits can be further inferred. If such information is leaked to an untrustworthy third party, it will seriously threaten the rights and interests of users. and safe. Therefore, it is very important to protect the user's location privacy. [0003] The co...

AI Technical Summary

Problems solved by technology

However, since the selected fake locations all have similar query probabilities, it may lead to a more regular distribution of fake locations, or multiple locations have the same location type, that is, the indistinguishability of k locations is small, so that the attacker can exploit fake locations. Location distribution characteristics and location semantics infer the real location

Moreover, the enhanced-DLS method makes the false location distribution spread as much as possible, does not consider the actual needs of users, and reasonably limits the area of ​​​​anonymous regions, which will generate a lot of computational overhead and seriously affect the service quality of query requests

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