A Semantic Location Privacy Protection Method Based on Privacy Preference in Road Network Environment

Abstract

The present invention is applicable to the technical field of privacy protection, and provides a semantic location privacy protection method based on privacy preference in a road network environment, including the following steps: receiving a location service query request sent by a user, and the location service query includes: user-defined privacy requirements; based on privacy requirements, an anonymous area is constructed, and the anonymous area is sent to the LBS server; the LBS server performs a location service query based on the anonymous area, and returns candidate query results to the TTP server; the TTP server returns to the LBS server based on the precise location of the user Candidate query results are filtered, and the filtered query results are returned to the user. On the premise of satisfying K-anonymity and L-segment diversity, by adjusting the threshold of privacy tolerance, it can effectively support the user's choice of privacy preference between the degree of location privacy protection and service quality, and fully meet the user's personalized privacy needs.

Description

technical field [0001] The invention belongs to the technical field of privacy protection, and provides a semantic location privacy protection method based on privacy preference in a road network environment. 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. Snapshot query is a common LBS service. Users upload their own location to obtain service information through mobile devices with precise positioning functions, such as querying 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 habits, occupations, and health. ...

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 semantic 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. , this method does not consider the user's privacy preference choice between the degree of location privacy protection and service quality, and cannot fully meet the user's personalized privacy needs

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