Confidential Benchmarking based on Multiparty Computation.

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 Damgard 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.