Privacy Preserving Moving KNN Queries

TL;DR

This paper introduces a privacy-preserving method for moving kNN queries that prevents trajectory tracking by hiding sensitive information and using an efficient algorithm, ensuring user privacy without sacrificing query performance.

Contribution

It presents the first solution to trajectory privacy in MkNN queries, including a novel confidence level mechanism and a faster algorithm for finding k nearest neighbors.

Findings

The proposed method effectively protects user trajectory privacy.

The new algorithm is at least twice as fast as existing solutions.

Experimental results confirm the approach's efficiency and privacy effectiveness.

Abstract

We present a novel approach that protects trajectory privacy of users who access location-based services through a moving k nearest neighbor (MkNN) query. An MkNN query continuously returns the k nearest data objects for a moving user (query point). Simply updating a user's imprecise location such as a region instead of the exact position to a location-based service provider (LSP) cannot ensure privacy of the user for an MkNN query: continuous disclosure of regions enables the LSP to follow a user's trajectory. We identify the problem of trajectory privacy that arises from the overlap of consecutive regions while requesting an MkNN query and provide the first solution to this problem. Our approach allows a user to specify the confidence level that represents a bound of how much more the user may need to travel than the actual kth nearest data object. By hiding a user's required confidence level and the required number of nearest data objects from an LSP, we develop a technique to prevent the LSP from tracking the user's trajectory for MkNN queries. We propose an efficient algorithm for the LSP to find k nearest data objects for a region with a user's specified confidence level, which is an essential component to evaluate an MkNN query in a privacy preserving manner; this algorithm is at least two times faster than the state-of-the-art algorithm. Extensive experimental studies validate the effectiveness of our trajectory privacy protection technique and the efficiency of our algorithm.