Time-Efficient Locally Relevant Geo-Location Privacy Protection
Chenxi Qiu, Ruiyao Liu, Primal Pappachan, Anna Squicciarini, Xinpeng, Xie

TL;DR
This paper introduces LR-Geo, a time-efficient geo-obfuscation method that localizes calculations to relevant locations, reducing computational load while maintaining privacy and utility in location sharing.
Contribution
The paper proposes a novel locally relevant geo-obfuscation approach that enhances efficiency and privacy by confining LP computations to local locations and using Benders' decomposition.
Findings
LR-Geo reduces computational time significantly.
It maintains geo-indistinguishability across users.
It improves data utility compared to existing methods.
Abstract
Geo-obfuscation serves as a location privacy protection mechanism (LPPM), enabling mobile users to share obfuscated locations with servers, rather than their exact locations. This method can protect users' location privacy when data breaches occur on the server side since the obfuscation process is irreversible. To reduce the utility loss caused by data obfuscation, linear programming (LP) is widely employed, which, however, might suffer from a polynomial explosion of decision variables, rendering it impractical in largescale geo-obfuscation applications. In this paper, we propose a new LPPM, called Locally Relevant Geo-obfuscation (LR-Geo), to optimize geo-obfuscation using LP in a time-efficient manner. This is achieved by confining the geo-obfuscation calculation for each user exclusively to the locally relevant (LR) locations to the user's actual location. Given the potential risk…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Security in Wireless Sensor Networks · Vehicular Ad Hoc Networks (VANETs)
