A MovingObject Index for Efficient Query Processing with Peer-Wise Location Privacy
Dan Lin, Christian S. Jensen, Rui Zhang, Lu Xiao, Jiaheng Lu

TL;DR
This paper introduces the PEB-tree, a new index structure that efficiently supports privacy-aware location queries by integrating location proximity with peer-wise privacy policies.
Contribution
It presents the PEB-tree index and algorithms for privacy-aware range and kNN queries, addressing efficiency issues in peer-wise location privacy.
Findings
PEB-tree improves query processing efficiency.
Algorithms effectively handle privacy policies.
Experimental results validate performance gains.
Abstract
With the growing use of location-based services, location privacy attracts increasing attention from users, industry, and the research community. While considerable effort has been devoted to inventing techniques that prevent service providers from knowing a user's exact location, relatively little attention has been paid to enabling so-called peer-wise privacy--the protection of a user's location from unauthorized peer users. This paper identifies an important efficiency problem in existing peer-privacy approaches that simply apply a filtering step to identify users that are located in a query range, but that do not want to disclose their location to the querying peer. To solve this problem, we propose a novel, privacy-policy enabled index called the PEB-tree that seamlessly integrates location proximity and policy compatibility. We propose efficient algorithms that use the PEB-tree…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Data Management and Algorithms · Mobile Crowdsensing and Crowdsourcing
