Privacy-Preserving Estimation of Users‘ Density Distribution in Location-based Services through Geo-indistinguishability (Geo-Indistinguishability를 활용한 위치 기반 서비스에서의 프라이버시 보존 사용자 분포도 예측)

Seung Min Song (송승민); Kim Jong Wook (김종욱)

doi:10.9708/jksci.2022.27.12.161

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

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2022, 27(12), pp.161-169
DOI : 10.9708/jksci.2022.27.12.161
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : October 20, 2022
Accepted : December 9, 2022
Published : December 30, 2022

Seung Min Song ¹, Kim Jong Wook ¹

¹상명대학교

Accredited

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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* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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