Privacy-Preserving Aggregate Queries for Optimal Location Selection

TL;DR

This paper introduces privacy-preserving protocols for location-based aggregate queries that enable businesses to perform location analytics without sharing sensitive data, using homomorphic encryption and differential privacy.

Contribution

It presents novel privacy-preserving protocols for location queries utilizing homomorphic encryption, with security proofs and practical performance evaluation.

Findings

Protocols are highly practical with real and synthetic datasets.

Protocols maintain privacy while enabling accurate aggregate queries.

Differential privacy can be integrated with manageable utility loss.

Abstract

Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for location analytics, such as finding the optimal location for a new branch. However, location data owners cannot share their data with other businesses, mainly due to privacy and legal concerns. In this paper, we propose privacy-preserving solutions in which location-based queries can be answered by data owners without sharing their data with other businesses and without accessing sensitive information such as the customer list of the businesses that send the query. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We prove the security of the protocols in semi-honest threat model. We also explain how to achieve differential privacy in the proposed protocols and discuss its impact on utility. We evaluate the performance of the protocols with real and synthetic datasets and show that the proposed solutions are highly practical. The proposed solutions will facilitate an effective sharing of sensitive data between entities and joint analytics in a wide range of applications without violating their customers' privacy.