• Title/Summary/Keyword: Geo-indistinguishability

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

  • Song, Seung Min;Kim, Jong Wook
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.12
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    • pp.161-169
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    • 2022
  • 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.