MS THESIS PRESENTATION: Privacy-Preserving Protocols for Aggregate Location Queries via Homomorphic Encryption and Multiparty Computation
(Supervisor: Asst. Prof. Dr. Erman Ayday)
Computer Engineering Department
Two main goals of the businesses are to serve their customers better and in the meantime, increase their profit. One of the ways that businesses can improve their services is using location information of their customers (e.g., positioning their facilities with an objective to minimize the average distance of their customers to their closest facilities). However, without the customer’s location data, it is impossible for businesses to achieve such goals. Luckily, in today’s world, large amounts of location data is collected by service providers such as telecommunication operators or mobile apps such as Swarm. Service providers are willing to share their data with businesses, doing this will violate the privacy of their customers. Here, we propose two new privacy-preserving schemes for businesses to utilize location data of their customers that is collected by location-based service providers (LBSPs). We utilize lattice based homomorphic encryption and multiparty computation for our new schemes and then we compare them with our existing scheme which is based on partial homomorphic encryption. In our protocols, we hide customer lists of businesses from LBSPs, locations of the customers from the businesses, and query result from LBSPs. In such a setting, we let the businesses send location-based queries to the LBSPs. In addition, we make the query result only available to the businesses and hide them from the LBSPs. We evaluate our proposed schemes to show that they are practical. We then compare our three protocols, discussing each one’s advantages and disadvantages and give use cases for all protocols. Our proposed schemes allow data sharing in a private manner and create the foundation for the future complex queries.
DATE: 11 July 2019, Thursday @ 14:30