A Survey Of differential privacy-based techniques and their applicability to location-Based services

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has led users to constantly generate various location data during their daily activities. Consequently, a growing interest has been seen in location-based services (LBSs), which aim to provide services adjusted to the current locations of users. However, location information may contain sensitive data; therefore, most users are reluctant to provide their exact location data to service providers. This has been identified as the most significant challenge in LBSs. Recently, differential privacy (DP) has emerged as a de facto standard for privacy preserving data processing. With its strong privacy guarantees, DP has been used in diverse areas such as the collection, analysis, and release of sensitive private data, and several variants of DP have been proposed in the literature. The main objective of this paper is to investigate the applicability of DP-based approaches in an LBS setting. In this paper, we first describe the basic concept of DP and then survey its three variants: (a) geo-indistinguishability, (b) private spatial decomposition, and (c) local differential privacy, which are designed or can be used to protect location privacy in LBSs. Furthermore, we explore the applicability of DP based schemes in protecting location privacy in different location data processing, collection, and publishing scenarios in LBSs. Finally, certain promising future research directions are discussed to spur further research in this area. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This article explores the use of mobile devices in generating location data and the issue of location privacy, then delves into the application of differential privacy methods and its three variants in the context of LBS.