Building a privacy-preserving benchmarking enterprise system

Abstract

Benchmarking is the process of comparing one's own performance to the statistics of a group of competitors, named peer group. It is a common and important process in the business world for many important business metrics, called key performance indicators (KPI). Privacy is of the utmost importance, since these KPIs allow the inference of sensitive information. Therefore several secure multi-party computation protocols for securely and privately computing the statistics of KPIs have recently been developed. These protocols are the basic building blocks for a privacy-preserving benchmarking system, but in order to complete an enterprise system that offers a benchmarking service to its customers more problems need to be solved. We first analyse how peer group participation impacts privacy and vice versa. Peer group formation is the process of forming sensible peer groups out of the set of subscribers. We characterise subscribers by a set of discrete criteria and therefore view the automatic peer group formation as a data clustering problem. We present a high-performance modification of k-means clustering that takes the minimum cluster size as an additional parameter which might be of independent interest. Our final approach is the first automatic peer group formation algorithm for an enterprise benchmarking system.