Deakin University
zhu-differentialprivate-2018.pdf (7.87 MB)

Differential private POI queries via Johnson-Lindenstrauss transform

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journal contribution
posted on 2018-05-27, 00:00 authored by Mengmeng Yang, Tianqing Zhu, Bo Liu, Yang Xiang, Wanlei Zhou
The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user's locations but other sensitive information, such as the user's interests or daily routines, which raises strong privacy concerns. Differential privacy is a well-acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user location privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. To solve this problem, we present a new privacy framework that includes a semi-trusted third party. Under our privacy framework, both the server and the third party only hold a part of the user's location information. Neither the server nor the third party knows the exact location of the user. In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, -NN and Range, are used to evaluate the method on two real-world data sets. Extensive comparisons against two representative differential privacy-based methods show that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.



IEEE access




29685 - 29699


Institute of Electrical and Electronics Engineers


Piscataway, N.J.





Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, IEEE