A Privacy-Preserving Benchmarking Platform

A privacy-preserving benchmarking platform is practically feasible, i.e. its performance is tolerable to the user on current hardware while fulfilling functional and security requirements. This dissertation designs, architects, and evaluates an implementation of such a platform. It contributes a novel (secure computation) benchmarking protocol, a novel method for computing peer groups, and a realistic evaluation of the first ever privacy-preserving benchmarking platform

Optimizations for Risk-Aware Secure Supply Chain Master Planning

Supply chain master planning strives for optimally aligned production, warehousing and transportation decisions across a multiple number of partners. Its execution in practice is limited by business partners' reluctance to share their vital business data. Secure Multi-Party Computation (SMC) can be used to make such collaborative computations privacy-preserving by applying cryptographic techniques. Thus, computation becomes acceptable in practice, but the performance of SMC remains critical for real world-sized problems. We assess the disclosure risk of the input and output data and then apply a protection level appropriate for the risk under the assumption that SMC at lower protection levels can be performed faster. This speeds up the secure computation and enables significant improvements in the supply chain

Optimizations for Risk-Aware Secure Supply Chain Master Planning

Supply chain master planning strives for optimally aligned production, warehousing and transportation decisions across a multiple number of partners. Its execution in practice is limited by business partners' reluctance to share their vital business data. Secure Multi-Party Computation (SMC) can be used to make such collaborative computations privacy-preserving by applying cryptographic techniques. Thus, computation becomes acceptable in practice, but the performance of SMC remains critical for real world-sized problems. We assess the disclosure risk of the input and output data and then apply a protection level appropriate for the risk under the assumption that SMC at lower protection levels can be performed faster. This speeds up the secure computation and enables significant improvements in the supply chain