PrivatePool: Privacy-Preserving Ridesharing
Paper i proceeding, 2017

Location-based services have seen tremendous developments over the recent years. These services have revolutionized transportation business, as witnessed by the success of Uber, Lyft, BlaBlaCar, and the like. Yet from the privacy point of view, the state of the art leaves much to be desired. The location of the user is typically shared with the service, opening up for privacy abuse, as in some recently publicized cases. This paper proposes PrivatePool, a model for privacy-preserving ridesharing. We develop secure multi-party computation techniques for endpoint and trajectory matching that allow dispensing with trust to third parties. At the same time, the users learn of a ride segment they can share and nothing else about other users’ location. We establish formal privacy guarantees and investigate how different riding patterns affect the privacy, utility, and performance tradeoffs between approaches based on the proximity of endpoints vs. proximity of trajectories.

Privacy-enhancing technologies

Location privacy

Författare

Per Hallgren

Informationssäkerhet

Claudio Orlandi

Aarhus Universitet

Andrei Sabelfeld

Informationssäkerhet

Proceedings - IEEE Computer Security Foundations Symposium

19401434 (ISSN)

276-291 8049726

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Data- och informationsvetenskap

DOI

10.1109/CSF.2017.24

ISBN

978-1-5386-3216-1

Mer information

Senast uppdaterat

2018-02-28