An Enhanced Approach to Cloud-based Privacy-preserving Benchmarking (Long Version)

TL;DR

This paper introduces an improved privacy-preserving benchmarking protocol using homomorphic encryption, allowing secure cloud-based comparison of KPIs with practical performance even under adverse conditions.

Contribution

It presents an enhanced homomorphic encryption-based benchmarking protocol that supports comprehensive statistical KPI comparisons while ensuring privacy and efficiency.

Findings

System supports mean, variance, median, max, and quartiles

Performs well under worst-case connection and security scenarios

Both theoretical and empirical evaluations confirm practicability

Abstract

Benchmarking is an important measure for companies to investigate their performance and to increase efficiency. As companies usually are reluctant to provide their key performance indicators (KPIs) for public benchmarks, privacy-preserving benchmarking systems are required. In this paper, we present an enhanced privacy-preserving benchmarking protocol that is based on homomorphic encryption. It enables cloud-based KPI comparison including the statistical measures mean, variance, median, maximum, best-in-class, bottom quartile, and top quartile. The theoretical and empirical evaluation of our benchmarking system underlines its practicability. Even under worst-case assumptions regarding connection quality and asymmetric encryption key-security, it fulfils the performance requirements of typical KPI benchmarking systems.