Name
Abstract | ||
|---|---|---|
In this paper, we propose a practical, privacy-preserving equality testing primitive which allows two users to learn if they share the same encrypted input data. Our protocol assumes no trust on a third party and/or other peers, and it is specifically suited for low-min entropy data (i.e., data that can be exhaustively searched by an attacker), such as encrypted users locations. We demonstrate that our primitive is secure and efficient: Two public-key exponentiations are required, per each user, for each equality testing. We give implementation results, showing that our primitive is practical in a multiple users scenario. Finally, we describe how we could use our primitive as a building block for a proximity testing buddy-finder service for social networks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11257-2_24 | PRIVACY IN STATISTICAL DATABASES, PSD 2014 |
Keywords | Field | DocType |
Equality testing, Location privacy, Buddy-finder social network, Location-based services, Geo-social applications | Internet privacy,Social network,Computer security,Computer science,Location-based service,Encryption,Third party,Information privacy | Conference |
Volume | ISSN | Citations |
8744 | 0302-9743 | 5 |
PageRank | References | Authors |
0.43 | 42 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emmanouil Magkos | 1 | 217 | 24.01 |
| panayiotis kotzanikolaou | 2 | 363 | 28.70 |
| Marios Magioladitis | 3 | 5 | 0.43 |
| Spyros Sioutas | 4 | 206 | 77.88 |
| Vassilios S. Verykios | 5 | 1402 | 96.96 |