Rawane Issa, Boston University

Abstract

Route recommendation services (e.g. Google maps) have become widespread and incorporate numerous factors (e.g. traffic and road closures) to compute optimal routes. Utilizing these services, however, comes at a cost to user's privacy: the service provider learns the source and destination locations of the user for every query that is submitted. Previous protocols for privacy preserving route recommendation algorithms have been constructed using tools such as garbled circuits and private information retrieval (PIR), but have had large overheads in latency and bandwidth.

In this work, we present an MPC protocol and architecture for route recommendation that guarantees: (1) low latency and bandwidth for user queries (2) scalability with respect to the number of users (3) independence from the underlying route recommendation algorithm. We provide a differential privacy-esque extension to our protocol in order to provide meaningful guarantees against inference attacks. Our protocol extends to other applications of key-value PIR protocols.

This work is implemented using JIFF, an easy to use general-purpose framework for implementing MPC protocols in a contemporary web technology stack."