Outsourcing MPC Precomputation for Location Privacy
(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:
https://lup.lub.lu.se/record/3a9d4fb2-6589-429c-b910-b17aefc4ed19
- author
- Oleynikov, Ivan ; Pagnin, Elena LU and Sabelfeld, Andrei
- organization
- publishing date
- 2022
- 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
- 2024-10-04 05:42:38
@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}}, }