Privacy-preserving collaborative analytics of location data

Abstract

Deriving meaningful insights from location data helps businesses make better decisions. While businesses must know the locations of their customers to perform location analytics, most businesses do not have this valuable data. Location data is typically collected by other services such as mobile telecommunication operators and location-based service providers. We develop scalable privacy-preserving solutions for collaborative analytics of location data. We propose two classes of approaches for location analytics when businesses do not have the location data of the customers. We illustrate both of our approaches in the context of optimal location selection for the new branches of businesses. The rst type of approach is retrieving the aggregate information about the customer locations from location data owners via privacy-preserving queries. We de ne aggregate queries that can be used in optimal location selection and we propose secure two-party protocols for processing these queries. The proposed protocols utilize partially homomorphic encryption as a building block and satisfy differential privacy. Our second approach is to generate synthetic location data in order to perform analytics without violating privacy of individuals. We propose a neighborhood-based data generation method which can be used by businesses for predicting the optimal location when they have partial information about customer locations. We also propose grid-based and clustering-based data generation methods which can be used by location data owners for publishing privacy-preserving synthetic location data. Proposed approaches facilitate running optimal location queries by businesses without knowing their customers' locations.