Method for protecting sensitive semantic location privacy for continuous query in road network environment

Abstract

The invention is applicable to the technical field of privacy protection, and provides a method for protecting sensitive semantic location privacy for continuous query in a road network environment. The method comprises the following steps: S1, receiving a location query request sent by a user; S2, judging whether the location query request is a first time location query request of the user, if the detection result is yes, constructing an anonymous user set based on the space-time similarity, and if the detection result is no, executing a step S3; S3, constructing a semantic secure anonymous area of the anonymous user set, and sending the semantic secure anonymous area to an LBS location server; S4, receiving candidate results returned by the LBS location server; and S5, filtering the candidate results based on a precise location of the user, and returning the filtered query result to the user. By adoption of the method provided by the invention, an attacker can be effectively prevented from using a semantic inference attack to attack the user location privacy under the continuous query location service; and by constructing the semantic secure anonymous area, the protection degreeof a K-anonymous algorithm on the user location privacy under the continuous query location service is enhanced.

Description

technical field [0001] The invention belongs to the technical field of privacy protection, and provides a method for protecting privacy of sensitive semantic locations under a road network environment with continuous query direction. Background technique [0002] With the continuous development of mobile technology, location-based services (Location-based services, LBS) have been gradually applied to a wider range of fields. Continuous query is a common LBS service. Users need to continuously upload their own location to obtain service information within the validity period of the query, for example: query the location information of the nearest hospitals in the surrounding area, navigation routes when driving, etc. However, while users enjoy the convenience brought by location-based services, they may face the risk of location privacy being stolen. By analyzing location semantic information, attackers are likely to infer users' privacy information such as hobbies, living ha...

AI Technical Summary

Problems solved by technology

However, while users enjoy the convenience brought by location-based services, they may face the risk of location privacy being stolen. By analyzing location semantic information, attackers are likely to infer users' privacy information such as hobbies, living habits, occupations, and health.

[0003] Most of the existing continuous query location privacy protection methods use K-anonymity and L-segment diversity methods, that is, the anonymous server replaces the precise location uploaded by the user with an anonymous area containing K users and L road segments, thereby improving location privacy security. However, this method does not consider the semantic location information under continuous query. The attacker matches the anonymous area with the city map, analyzes the semantic location in the anonymous area, and conducts semantic inference attacks to infer the semantic location information of the user and reduce privacy. degree of protection

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