Anonymized Location Query Processing in Road Networks
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Solution Overview
Problem
Conventional location-based services (LBS) methods compromise user privacy by revealing location information to servers, leading to potential tracking risks and increased computational and communication costs, as they do not effectively anonymize user locations during k-nearest queries in road networks.
Innovation Solution
An Enhanced Location Anonymizer (ELA) server anonymizes user locations before transferring query requests to a Location Based Service (LBS) server, allowing the ELA server to recalculate query results when the user moves out of a safe zone, reducing the computational and communication loads on the LBS server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional LBS methods are used to process k-nearest queries, then query processing can be performed, but user privacy is compromised and location information is revealed to servers
Solution Approach 1:
The patent introduces an Enhanced Location Anonymizer (ELA) server as an intermediary between the mobile object and the LBS server. The ELA server receives the mobile object's precise location, anonymizes it by determining an anonymized location region, and then processes the k-nearest query. This intermediary structure allows the LBS server to receive queries without directly accessing the mobile object's precise location information, thereby protecting user privacy while enabling query processing.
2Measurement precision
If precise user location is transmitted to the server, then accurate query results can be obtained, but computational and communication costs increase
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different spatial regions. Instead of processing queries from all locations uniformly, the system determines an anonymized location region and processes queries locally within this region. The ELA server maintains the mobile object's location within the anonymized region and only processes queries when the object moves outside this region, reducing unnecessary computational and communication overhead while maintaining query accuracy within the local anonymized area.
3Productivity
If the LBS server processes all k-nearest queries directly, then query processing is centralized, but communication load and network influence increase
Solution Approach 1:
The patent segments the query processing function by dividing it between the ELA server and the LBS server. The ELA server handles location anonymization and maintains the mobile object's anonymized location, while the LBS server handles the actual k-nearest query processing. This segmentation allows the system to reduce communication load by keeping the mobile object within an anonymized location region and only updating when necessary, rather than continuously communicating precise location data.
Data Source
AI summary
The present invention is directed to a method of calculating the results of a moving k-nearest query and safe exit locations in a road network, and more particularly to a method and apparatus that receive a request from a client terminal and provide the results of a k-nearest query, a safe zone and safe exit locations together. In the present invention, a query is processed in a server, a request for a query from a querying user and the location information of the querying user are received from an LBS, the location information of the querying user is anonymized and then the query request is transferred to the server, and query results corresponding to the anonymized location information of the querying user are received from the server and then transferred to the querying user, thereby protecting the location information of the querying user.