TY - GEN
T1 - Private Proximity Retrieval
AU - Etzion, Tuvi
AU - Gnilke, Oliver W.
AU - Karpuk, David
AU - Yaakobi, Eitan
AU - Zhang, Yiwei
N1 - Funding Information: Authors appear in alphabetical order. Research supported in part by the Israel Science Foundation (ISF) grant No. 1817/18 and the NSF-BSF grant No. 2016692. Y. Zhang was also supported in part by a Technion Fellowship. This research of T. Etzion, E. Yaakobi, and Y. Zhang was also partially supported by the Technion Hiroshi Fujiwara cyber security research center and the Israel cyber directorate. Part of this work was carried out while Oliver Gnilke was with the Department of Mathematics and Systems Analysis, Aalto University, Finland. Funding Information: Research supported in part by the Israel Science Foundation (ISF) grant No. 1817/18 and the NSF-BSF grant No. 2016692. Y. Zhang was also supported in part by a Technion Fellowship. Publisher Copyright: © 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - A private proximity retrieval (PPR) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance r from the user's record x. The user's privacy at each server is given by the fraction of the record x that is kept private. The distortion of a PPR scheme measures how accurately the user can calculate the identities of the desired files. We assume that each server stores a copy of the database. This paper studies protocols that offer trade-offs between perfect privacy and low computational complexity and storage.In this paper, this study is initiated. The work focuses on the case when the records are binary vectors together with the Hamming distance. In particular, for a given privacy level, we investigate the minimum number of servers that guarantee a prescribed distortion value. The collusions of pairs of servers as well as other distance measures are investigated.
AB - A private proximity retrieval (PPR) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance r from the user's record x. The user's privacy at each server is given by the fraction of the record x that is kept private. The distortion of a PPR scheme measures how accurately the user can calculate the identities of the desired files. We assume that each server stores a copy of the database. This paper studies protocols that offer trade-offs between perfect privacy and low computational complexity and storage.In this paper, this study is initiated. The work focuses on the case when the records are binary vectors together with the Hamming distance. In particular, for a given privacy level, we investigate the minimum number of servers that guarantee a prescribed distortion value. The collusions of pairs of servers as well as other distance measures are investigated.
UR - https://www.scopus.com/pages/publications/85073154830
U2 - 10.1109/ISIT.2019.8849249
DO - 10.1109/ISIT.2019.8849249
M3 - Conference contribution
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 2119
EP - 2123
BT - 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
T2 - 2019 IEEE International Symposium on Information Theory, ISIT 2019
Y2 - 7 July 2019 through 12 July 2019
ER -