TY - GEN
T1 - A multi-party protocol for privacy-preserving range queries
AU - Sepehri, Maryam
AU - Cimato, Stelvio
AU - Damiani, Ernesto
N1 - Funding Information:
This work was partly supported by the EU project CUMULUS (contract n. FP7-318580).
PY - 2014
Y1 - 2014
N2 - Privacy-preserving query processing (PPQP) techniques are increasingly important in collaborative scenarios, where users need to execute queries on large amount of data shared among different parties who do not want to disclose private data to the others. In many cases, secure multi-party computation (SMC) protocols can be applied, but the resulting solutions are known to suffer from high computation and communication costs. In this paper, we describe a scalable protocol for performing queries in distributed data while respecting the data owners' privacy. Our solution is applicable both to equality and range queries, and relies on a bucketization technique in order to reduce time complexity. We show the effectiveness of our approach through theoretical and practical analysis.
AB - Privacy-preserving query processing (PPQP) techniques are increasingly important in collaborative scenarios, where users need to execute queries on large amount of data shared among different parties who do not want to disclose private data to the others. In many cases, secure multi-party computation (SMC) protocols can be applied, but the resulting solutions are known to suffer from high computation and communication costs. In this paper, we describe a scalable protocol for performing queries in distributed data while respecting the data owners' privacy. Our solution is applicable both to equality and range queries, and relies on a bucketization technique in order to reduce time complexity. We show the effectiveness of our approach through theoretical and practical analysis.
KW - Privacy-preserving query processing and bucketization
KW - Range query
KW - Secure multi-party computation
UR - http://www.scopus.com/inward/record.url?scp=84902477698&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-06811-4_15
DO - 10.1007/978-3-319-06811-4_15
M3 - Conference contribution
AN - SCOPUS:84902477698
SN - 9783319068107
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 108
EP - 120
BT - Secure Data Management - 10th VLDB Workshop, SDM 2013, Proceedings
PB - Springer Verlag
T2 - 10th VLDB Workshop on Secure Data Management, SDM 2013
Y2 - 30 August 2013 through 30 August 2013
ER -