Department of Computer Engineering
CS 690 SEMINAR
Privacy-Preserving Location Analytics as a Service
Computer Engineering Department
Today, vast amounts of location data are collected by various service providers. The data owners, e.g., location-based service providers and mobile network operators, have a good idea of where their customers are most of the time. Other businesses also want to use this information for location analytics, such as optimal location selection, e.g., finding the optimal location for the new branch of a business based on the locations of existing customers between specific hours of the day. However, location data owners can not directly share their data with other businesses, mainly due to privacy and legal concerns. Conversely, businesses can not share their customer lists with location-based service providers due to similar concerns. In this work, we propose privacy-preserving solutions in which location-based queries can be executed and answered by location data owners without sharing the data and without accessing the customer list of the businesses that send the query. The proposed protocols aim to ensure that people's whereabouts are not revealed to the businesses and that the result of the query can only be learned by the business that sends the query. Hence even the location-based service provider cannot see the query result. We utilize a partially homomorphic cryptosystem as the building block of the proposed protocols. We show that the proposed solutions are highly practical and can run in less than a minute on datasets that include millions of individuals. The proposed solutions will facilitate the sharing of sensitive data between entities without violating their customers' privacy.
DATE: 22 February, 2016, Monday @ 16:50