TY - GEN
T1 - Secure and efficient k-NN queries
AU - Asif, Hafiz
AU - Vaidya, Jaideep
AU - Shafiq, Basit
AU - Adam, Nabil
N1 - Funding Information:
The work of Shafiq is supported by HEC grant under the PAK-US Science and Technology Cooperation Program and by HEC NRPU grant. The work of Adam is supported by the National Academies of Sciences, Engineering, and Medicine under the PAK-US Science and Technology Cooperation Program. The work of Vaidya and Asif is supported in part by the National Science Foundation Grant CNS-1422501 and the National Institutes of Health Award R01GM118574. The content is solely the responsibility of the authors and does not necessarily represent the official views of the agencies funding the research.
Publisher Copyright:
© IFIP International Federation for Information Processing 2017.
PY - 2017
Y1 - 2017
N2 - Given the morass of available data, ranking and best match queries are often used to find records of interest. As such, k-NN queries, which give the k closest matches to a query point, are of particular interest, and have many applications. We study this problem in the context of the financial sector, wherein an investment portfolio database is queried for matching portfolios. Given the sensitivity of the information involved, our key contribution is to develop a secure k-NN computation protocol that can enable the computation k-NN queries in a distributed multi-party environment while taking domain semantics into account. The experimental results show that the proposed protocols are extremely efficient.
AB - Given the morass of available data, ranking and best match queries are often used to find records of interest. As such, k-NN queries, which give the k closest matches to a query point, are of particular interest, and have many applications. We study this problem in the context of the financial sector, wherein an investment portfolio database is queried for matching portfolios. Given the sensitivity of the information involved, our key contribution is to develop a secure k-NN computation protocol that can enable the computation k-NN queries in a distributed multi-party environment while taking domain semantics into account. The experimental results show that the proposed protocols are extremely efficient.
KW - Distributed computation
KW - K-NN classification
KW - K-NN queries
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=85019668716&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019668716&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58469-0_11
DO - 10.1007/978-3-319-58469-0_11
M3 - Conference contribution
AN - SCOPUS:85019668716
SN - 9783319584683
T3 - IFIP Advances in Information and Communication Technology
SP - 155
EP - 170
BT - ICT Systems Security and Privacy Protection - 32nd IFIP TC 11 International Conference, SEC 2017, Proceedings
A2 - De Capitani di Vimercati, Sabrina
A2 - Martinelli, Fabio
PB - Springer New York LLC
T2 - 32nd International Conference on ICT Systems Security and Privacy Protection, IFIP SEC 2017
Y2 - 29 May 2017 through 31 May 2017
ER -