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@Inproceedings { 2023_pennekamp_benchmarking_comparison,
   title = {Designing Secure and Privacy-Preserving Information Systems for Industry Benchmarking},
   year = {2023},
   month = {6},
   day = {15},
   volume = {13901},
   pages = {489-505},
   abstract = {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.},
   note = {Lecture Notes in Computer Science (LNCS), Volume 13901},
   keywords = {real-world computing; trusted execution environments; homomorphic encryption; key performance indicators; benchmarking},
   tags = {internet-of-production},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-pennekamp-industry-benchmarking.pdf},
   publisher = {Springer},
   booktitle = {Proceedings of the 35th International Conference on Advanced Information Systems Engineering (CAiSE '23), June 12-16, 2023, Zaragoza, Spain},
   event_place = {Zaragoza, Spain},
   event_name = {35th International Conference on Advanced Information Systems Engineering (CAiSE '23)},
   event_date = {June 12-16, 2023},
   ISBN = {978-3-031-34559-3},
   ISSN = {0302-9743},
   DOI = {10.1007/978-3-031-34560-9_29},
   reviewed = {1},
   author = {Pennekamp, Jan and Lohm{\"o}ller, Johannes and Vlad, Eduard and Loos, Joscha and Rodemann, Niklas and Sapel, Patrick and Fink, Ina Berenice and Schmitz, Seth and Hopmann, Christian and Jarke, Matthias and Schuh, G{\"u}nther and Wehrle, Klaus and Henze, Martin}
}

@Inproceedings { 2020_pennekamp_benchmarking,
   title = {Revisiting the Privacy Needs of Real-World Applicable Company Benchmarking},
   year = {2020},
   month = {12},
   day = {15},
   pages = {31-44},
   abstract = {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.},
   keywords = {practical encrypted computing; homomorphic encryption; algorithm confidentiality; benchmarking; key performance indicators; industrial application; Internet of Production},
   tags = {internet-of-production},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-pennekamp-company-benchmarking.pdf},
   web_url = {https://eprint.iacr.org/2020/1512},
   publisher = {HomomorphicEncryption.org},
   booktitle = {Proceedings of the 8th Workshop on Encrypted Computing \& Applied Homomorphic Cryptography (WAHC '20), December 15, 2020, Virtual Event},
   event_place = {Virtual Event},
   event_date = {December 15, 2020},
   ISBN = {978-3-00-067798-4},
   DOI = {10.25835/0072999},
   reviewed = {1},
   author = {Pennekamp, Jan and Sapel, Patrick and Fink, Ina Berenice and Wagner, Simon and Reuter, Sebastian and Hopmann, Christian and Wehrle, Klaus and Henze, Martin}
}