Location based services (LBS) are becoming increasingly popular, ranging from device tracking to vehicle collision detection and to a wide variety of social LBS. As devices become increasingly interconnected, and the majority shares location information with different parties, often unbeknownst the user, it becomes increasingly important that location information can be used without violating user privacy.
While state-of-the-art privacy-preserving techniques make use of pragmatic approaches to obfuscating location data, they largely fall short to provide rigorous privacy guarantees.
DecentLP will build a solid foundation for location privacy. DecentLP will provide unhampered functionality while providing rigorous and robust privacy by means of secure multi-party computation (SMC), where participants can jointly compute a function based on private inputs. While most existing location privacy approaches focus on mitigating information disclosure, DecentLP will remove unintended information disclosure entirely. DecentLP will break away from centralized trust and will enable user privacy while not relying on trust to governments, service providers, or infrastructure owners.
There is an unsettling gap between the two communities dealing with location privacy and SMC. DecentLP will bridge this gap to achieve robust location-privacy by novel, rigorous, and efficient SMC techniques.
at Computer Science and Engineering, Software Technology (Chalmers)
Funding years 2015–2018
Area of Advance
Chalmers Driving Force