Outsourcing MPC Precomputation for Location Privacy

Ivan Oleynikov, Elena Pagnin, Andrei Sabelfeld

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceedingPeer review


Proximity testing is at the core of sev-eral Location-Based Services (LBS) offered by, e.g., Uber, Facebook, and BlaBlaCar, as it determines closeness to a target. Unfortunately, modern LBS demand not only that clients disclose their locations in plain, but also to trust that the services will not abuse this information. These requirements are unfounded as there are ways to perform proximity testing without revealing one's location. We propose POLAR, a protocol that imple-ments privacy-preserving proximity testing for LBS. POLAR is suitable for clients running mo-bile devices, and relies on a careful combination of three well-established multiparty computation protocols and lightweight cryptography. A point of originality is the inclusion of two servers into the proximity testing. The servers may aid multiple pairs of clients and contribute towards enhancing privacy, improving efficiency, and reducing the run-ning time of clients' procedures.
Titel på värdpublikation2022 IEEE European Symposium on Security and Privacy Workshops (EuroSPW)
FörlagIEEE - Institute of Electrical and Electronics Engineers Inc.
Antal sidor10
ISBN (elektroniskt)978-1-6654-9560-8
ISBN (tryckt)978-1-6654-9561-5
StatusPublished - 2022
Evenemang7th IEEE European Symposium on Security and Privacy, EuroS&P 2022 - Genoa, Italien
Varaktighet: 2022 juni 62022 juni 10


Konferens7th IEEE European Symposium on Security and Privacy, EuroS&P 2022

Ämnesklassifikation (UKÄ)

  • Datavetenskap (datalogi)


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