Privacy preserving localization for smart automotive systems

Abstract

This paper presents the first provably secure localization method for smart automotive systems. Using this method, a lost car can compute its location with assistance from three nearby cars while the locations of all the participating cars including the lost car remain private. This localization application is one of the very first location-based services that does not sacrifice accuracy to maintain privacy. The secure location is computed using a protocol utilizing Yao's Garbled Circuit (GC) that allows two parties to jointly compute a function on their private inputs. We design and optimize GC netlists of the functions required for computation of location by leveraging conventional logic synthesis tools. Proof-of-concept implementation of the protocol shows that the complete operation can be performed within only 550 ms. The fast computing time enables practical localization of moving cars.