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http://dx.doi.org/10.9708/jksci.2022.27.12.161

Privacy-Preserving Estimation of Users' Density Distribution in Location-based Services through Geo-indistinguishability  

Song, Seung Min (Dept. of Computer Science, Sangmyung University)
Kim, Jong Wook (Dept. of Computer Science, Sangmyung University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.12, 2022 , pp. 161-169 More about this Journal
Abstract
With the development of mobile devices and global positioning systems, various location-based services can be utilized, which collects user's location information and provides services based on it. In this process, there is a risk of personal sensitive information being exposed to the outside, and thus Geo-indistinguishability (Geo-Ind), which protect location privacy of LBS users by perturbing their true location, is widely used. However, owing to the data perturbation mechanism of Geo-Ind, it is hard to accurately obtain the density distribution of LBS users from the collection of perturbed location data. Thus, in this paper, we aim to develop a novel method which enables to effectively compute the user density distribution from perturbed location dataset collected under Geo-Ind. In particular, the proposed method leverages Expectation-Maximization(EM) algorithm to precisely estimate the density disribution of LBS users from perturbed location dataset. Experimental results on real world datasets show that our proposed method achieves significantly better performance than a baseline approach.
Keywords
Location-based services; Location data; Data Privacy; Geo-indistinguishability;
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