Confidential benchmarking based on multiparty computation
Publikation: Working paper › Forskning
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Confidential benchmarking based on multiparty computation. / Damgård, Ivan Bjerre; Damgård, Kasper Lyneborg; Nielsen, Kurt; Nordholt, Peter Sebastian; Toft, Tomas.
International Association for Cryptologic Research, 2015.Publikation: Working paper › Forskning
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TY - UNPB
T1 - Confidential benchmarking based on multiparty computation
AU - Damgård, Ivan Bjerre
AU - Damgård, Kasper Lyneborg
AU - Nielsen, Kurt
AU - Nordholt, Peter Sebastian
AU - Toft, Tomas
PY - 2015/10/16
Y1 - 2015/10/16
N2 - We report on the design and implementation of a system that uses multiparty computation to enable banks to benchmark their customers' confidential performance data against a large representative set of confidential performance data from a consultancy house. The system ensures that both the banks' and the consultancy house's data stays confidential, the banks as clients learn nothing but the computed benchmarking score. In the concrete business application, the developed prototype help Danish banks to find the most efficient customers among a large and challenging group of agricultural customers with too much debt. We propose a model based on linear programming for doing the benchmarking and implement it using the SPDZ protocol by Damgård et al., which we modify using a new idea that allows clients to supply data and get output without having to participate in the preprocessing phase and without keeping state during the computation. We ran the system with two servers doing the secure computation using a database with information on about 2500 users. Answers arrived in about 25 seconds.
AB - We report on the design and implementation of a system that uses multiparty computation to enable banks to benchmark their customers' confidential performance data against a large representative set of confidential performance data from a consultancy house. The system ensures that both the banks' and the consultancy house's data stays confidential, the banks as clients learn nothing but the computed benchmarking score. In the concrete business application, the developed prototype help Danish banks to find the most efficient customers among a large and challenging group of agricultural customers with too much debt. We propose a model based on linear programming for doing the benchmarking and implement it using the SPDZ protocol by Damgård et al., which we modify using a new idea that allows clients to supply data and get output without having to participate in the preprocessing phase and without keeping state during the computation. We ran the system with two servers doing the secure computation using a database with information on about 2500 users. Answers arrived in about 25 seconds.
KW - Faculty of Science
KW - Implementation
KW - benchmarking
KW - multiparty computation
KW - secure computation
KW - linear programming
KW - simplex
KW - SPDZ
M3 - Working paper
T3 - Cryptology ePrint Archive
BT - Confidential benchmarking based on multiparty computation
PB - International Association for Cryptologic Research
ER -
ID: 146195786