Privacy Preservation of Location Information Based on MinHash Algorithm in Online Ride-Hailing Services
Miaomiao Li, Licheng Wang · 2018
In the era of big data, the development of location-aware technologies such as mobile communications, sensing devices, and mobile positioning has created big data for location. It has brought great convenience to people's production and daily life. But, users' location information used in LBS (Location-Based Services) can easily lead to the personal privacy leakage. Therefore, the privacy protection of location based big data is increasingly concerned by all sectors of society. The privacy preservation issues of the user location information in the popular online ride-hailing field will be discussed in this paper, and the ride-hailing model discussed here refers to the dispatch model. It is considered that users would rather use the landmark entities around them to describe their approximate location than their exact location when they are using a car navigation platform. Then, a complete location privacy preservation scheme based on the MinHash algorithm (LPPM in short) is proposed in this paper. LPPM can not only hide the location information effectively but also improve the speed of distance calculation between users and drivers. The core idea of LPPM is to select a certain range of landmarks near the user's exact location as his location features, and to use the MinHash algorithm to optimize the similarity assessment method between feature sets. The security analyses indicate that LPPM is of high security, and the final experimental results confirm that LPPM is effective.