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

Efficient Cloud-based Secret Shuffling via Homomorphic Encryption

When working with joint collections of confidential data from multiple sources, e.g., in cloud-based multi-party computation scenarios, the ownership relation between data providers and their inputs itself is confidential information. Protecting data providers' privacy desires a function for secretly shuffling the data collection. We present the first efficient secure multi-party computation protocol for secret shuffling in scenarios with a central server. Based on a novel approach to random index distribution, our solution enables the randomization of the order of a sequence of encrypted data such that no observer can map between elements of the original sequence and the shuffled sequence with probability better than guessing. It allows for shuffling data encrypted under an additively homomorphic cryptosystem with constant round complexity and linear computational complexity. Being a general-purpose protocol, it is of relevance for a variety of practical use cases

Revisiting the Privacy Needs of Real-World Applicable Company Benchmarking

Benchmarking the performance of companies is essential to identify improvement potentials in various industries. Due to a competitive environment, this process imposes strong privacy needs, as leaked business secrets can have devastating effects on participating companies. Consequently, related work proposes to protect sensitive input data of companies using secure multi-party computation or homomorphic encryption. However, related work so far does not consider that also the benchmarking algorithm, used in today\u27s applied real-world scenarios to compute all relevant statistics, itself contains significant intellectual property, and thus needs to be protected. Addressing this issue, we present PCB — a practical design for Privacy-preserving Company Benchmarking that utilizes homomorphic encryption and a privacy proxy — which is specifically tailored for realistic real-world applications in which we protect companies\u27 sensitive input data and the valuable algorithms used to compute underlying key performance indicators. We evaluate PCB\u27s performance using synthetic measurements and showcase its applicability alongside an actual company benchmarking performed in the domain of injection molding, covering 48 distinct key performance indicators calculated out of hundreds of different input values. By protecting the privacy of all participants, we enable them to fully profit from the benefits of company benchmarking