Privacy-Preserving Aggregate Queries for Optimal Location Selection
This addresses privacy and legal barriers for businesses needing location data analytics, though it is incremental as it builds on existing cryptographic and privacy techniques.
The paper tackles the problem of enabling businesses to perform location analytics, such as optimal branch placement, without sharing sensitive location data due to privacy concerns, by proposing privacy-preserving protocols using partially homomorphic encryption and differential privacy, and demonstrates their practicality with evaluations on real and synthetic datasets.
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.