This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-07-03 Creation time: 21-37-19 --- Number of references 3 inproceedings 2023_pennekamp_benchmarking_comparison 2023 6 15 13901 489-505 Benchmarking is an essential tool for industrial organizations to identify potentials that allows them to improve their competitive position through operational and strategic means. However, the handling of sensitive information, in terms of (i) internal company data and (ii) the underlying algorithm to compute the benchmark, demands strict (technical) confidentiality guarantees—an aspect that existing approaches fail to address adequately. Still, advances in private computing provide us with building blocks to reliably secure even complex computations and their inputs, as present in industry benchmarks. In this paper, we thus compare two promising and fundamentally different concepts (hardware- and software-based) to realize privacy-preserving benchmarks. Thereby, we provide detailed insights into the concept-specific benefits. Our evaluation of two real-world use cases from different industries underlines that realizing and deploying secure information systems for industry benchmarking is possible with today's building blocks from private computing. Lecture Notes in Computer Science (LNCS), Volume 13901 real-world computing; trusted execution environments; homomorphic encryption; key performance indicators; benchmarking internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-pennekamp-industry-benchmarking.pdf Springer Proceedings of the 35th International Conference on Advanced Information Systems Engineering (CAiSE '23), June 12-16, 2023, Zaragoza, Spain Zaragoza, Spain 35th International Conference on Advanced Information Systems Engineering (CAiSE '23) June 12-16, 2023 978-3-031-34559-3 0302-9743 10.1007/978-3-031-34560-9_29 1 JanPennekamp JohannesLohmöller EduardVlad JoschaLoos NiklasRodemann PatrickSapel Ina BereniceFink SethSchmitz ChristianHopmann MatthiasJarke GüntherSchuh KlausWehrle MartinHenze inproceedings 2020_pennekamp_benchmarking 2020 12 15 31-44 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's 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' sensitive input data and the valuable algorithms used to compute underlying key performance indicators. We evaluate PCB's 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. practical encrypted computing; homomorphic encryption; algorithm confidentiality; benchmarking; key performance indicators; industrial application; Internet of Production internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-pennekamp-company-benchmarking.pdf https://eprint.iacr.org/2020/1512 HomomorphicEncryption.org Proceedings of the 8th Workshop on Encrypted Computing & Applied Homomorphic Cryptography (WAHC '20), December 15, 2020, Virtual Event Virtual Event December 15, 2020 978-3-00-067798-4 10.25835/0072999 1 JanPennekamp PatrickSapel Ina BereniceFink SimonWagner SebastianReuter ChristianHopmann KlausWehrle MartinHenze inproceedings 2020_pennekamp_parameter_exchange 2020 12 10 510-525 Nowadays, collaborations between industrial companies always go hand in hand with trust issues, i.e., exchanging valuable production data entails the risk of improper use of potentially sensitive information. Therefore, companies hesitate to offer their production data, e.g., process parameters that would allow other companies to establish new production lines faster, against a quid pro quo. Nevertheless, the expected benefits of industrial collaboration, data exchanges, and the utilization of external knowledge are significant. In this paper, we introduce our Bloom filter-based Parameter Exchange (BPE), which enables companies to exchange process parameters privacy-preservingly. We demonstrate the applicability of our platform based on two distinct real-world use cases: injection molding and machine tools. We show that BPE is both scalable and deployable for different needs to foster industrial collaborations. Thereby, we reward data-providing companies with payments while preserving their valuable data and reducing the risks of data leakage. secure industrial collaboration; Bloom filter; oblivious transfer; Internet of Production internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-pennekamp-parameter-exchange.pdf ACM Proceedings of the 36th Annual Computer Security Applications Conference (ACSAC '20), December 7-11, 2020, Austin, TX, USA Austin, TX, USA December 7-11, 2020 978-1-4503-8858-0/20/12 10.1145/3427228.3427248 1 JanPennekamp ErikBuchholz YannikLockner MarkusDahlmanns TiandongXi MarcelFey ChristianBrecher ChristianHopmann KlausWehrle