An Enhanced Approach to Cloud-based Privacy-preserving Benchmarking (Long Version)
This addresses the need for secure and efficient benchmarking systems for companies, though it appears incremental as it builds on existing homomorphic encryption methods.
The paper tackles the problem of companies being unwilling to share sensitive key performance indicators (KPIs) for benchmarking by proposing an enhanced privacy-preserving protocol based on homomorphic encryption, which enables cloud-based comparison of statistical measures like mean and variance while meeting performance requirements even under worst-case conditions.
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.