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Outsourcing MPC Precomputation for Location Privacy

Oleynikov, Ivan ; Pagnin, Elena LU orcid and Sabelfeld, Andrei (2022) 7th IEEE European Symposium on Security and Privacy, EuroS&P 2022 p.504-513
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2022 IEEE European Symposium on Security and Privacy Workshops (EuroSPW)
pages
10 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
7th IEEE European Symposium on Security and Privacy, EuroS&P 2022
conference location
Genoa, Italy
conference dates
2022-06-06 - 2022-06-10
external identifiers
  • scopus:85134183349
ISBN
978-1-6654-9561-5
978-1-6654-9560-8
DOI
10.1109/EuroSPW55150.2022.00060
project
Säkra mjukvaruuppdateringar för den smarta staden
language
English
LU publication?
yes
id
3a9d4fb2-6589-429c-b910-b17aefc4ed19
date added to LUP
2022-07-07 13:57:09
date last changed
2024-06-13 17:58:57
@inproceedings{3a9d4fb2-6589-429c-b910-b17aefc4ed19,
  abstract     = {{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.}},
  author       = {{Oleynikov, Ivan and Pagnin, Elena and Sabelfeld, Andrei}},
  booktitle    = {{2022 IEEE European Symposium on Security and Privacy Workshops (EuroSPW)}},
  isbn         = {{978-1-6654-9561-5}},
  language     = {{eng}},
  pages        = {{504--513}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Outsourcing MPC Precomputation for Location Privacy}},
  url          = {{http://dx.doi.org/10.1109/EuroSPW55150.2022.00060}},
  doi          = {{10.1109/EuroSPW55150.2022.00060}},
  year         = {{2022}},
}