A MovingObject Index for Efficient Query Processing with Peer-Wise Location Privacy

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 for processing privacy-aware range and kNN queries. Extensive experiments suggest that the PEB-tree enables efficient query processing.