Confidential benchmarking based on multiparty computation

TY - GEN

T1 - Confidential benchmarking based on multiparty computation

AU - Damgård, Ivan

AU - Damgård, Kasper Lyneborg

AU - Nielsen, Kurt

AU - Nordholt, Peter Sebastian

AU - Toft, Tomas

N1 - Conference code: 20

PY - 2017

Y1 - 2017

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 helps 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 s.

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 helps 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 s.

U2 - 10.1007/978-3-662-54970-4_10

DO - 10.1007/978-3-662-54970-4_10

M3 - Article in proceedings

SN - 978-3-662-54970-4

T3 - Lecture notes in computer science

SP - 169

EP - 187

BT - International Conference on Financial Cryptography and Data Security

A2 - Grossklags, Jens

A2 - Preneel, Bart

PB - Springer

T2 - Financial Cryptography and Data Security, International Conference, 2016

Y2 - 22 February 2016 through 26 February 2016

ER -