Location privacy protection method based on virtual trajectory generation mechanism

Abstract

The invention discloses a position privacy protection method based on a virtual track generation mechanism, which comprises the following steps of: generating k-1 virtual users living in different places and indistinguishable from a real user by using the virtual track generation mechanism at a client according to a user portrait of the real user and a map API (Application Program Interface); and generating a false track which is indistinguishable from the real track for a user to use in LBS query. According to the method, the interest point information of the real user and the public interest point information of the known map are combined through a virtual track generation mechanism, the virtual user with the same behavior and moving mode as the real user is generated, and the position privacy of the real user is effectively protected in continuous LSP query.

Description

technical field [0001] The present invention relates to the field of computer science and technology, in particular to a method for protecting location privacy based on a virtual trajectory generation mechanism. Background technique [0002] In recent years, with the development of mobile Internet technology, the application of Location Based Service (LBS) has widely benefited the fields of commerce, people's livelihood, and medical care. Mobile users can obtain the current location or Point of Interest (POI) information near a certain location through the mobile application provided by the Location-based Service Provider (LSP). The app searches for nearby restaurants, hotels, movie theaters, and more. However, when the user makes continuous location service requests, the attacker can infer the user's personal privacy information, such as home address, living habits, religious beliefs and other information, based on the user's continuous location information. The "delistin...

AI Technical Summary

Problems solved by technology

Mobile users send encrypted location information to location service providers, which will cause a huge computational overhead on the server.

2. In the location privacy protection scheme based on space-time concealment, mobile users send a large-scale range instead of their actual location to the location service provider, which will lead to inaccurate user query results and reduce the user's service quality level

However, with the development of big data, artificial intelligence and other technologies, the k-anonymity method cannot resist data mining attacks, which will lead to the leakage of mobile users' location privacy.

Even if some privacy protection technologies are used in the LBS query process, some of these methods will generate huge computing overhead, some will reduce the user's service quality level, and some will not be able to resist data mining attacks

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