Filtering for Private Collaborative Benchmarking [chapter]

Florian Kerschbaum, Orestis Terzidis
2006 Lecture Notes in Computer Science  
Collaborative Benchmarking is an important issue for modern enterprises, but the business performance quantities used as input are often highly confidential. Secure Multi-Party Computation can offer protocols that can compute benchmarks without leaking the input variables. Benchmarking is a process of comparing to the "best", so often it is necessary to only include the k-best enterprises for computing a benchmark to not distort the result with some outlying performances. We present a protocol
more » ... hat can be used as a filter, before running any collaborative benchmarking protocol that restricts the participants to the k best values. Our protocol doesn't use the general circuit construction technique for SMC aiming to optimize performance. As building blocks we present the fastest implementation of Yao's millionaires' protocol and a protocol that achieves a fair shuffle in O(log n) rounds.
doi:10.1007/11766155_29 fatcat:jhso5eqnq5abrmrs5dwxdny6sq