This file was created by the TYPO3 extension bib --- Timezone: UTC Creation date: 2025-03-07 Creation time: 03-03-55 --- Number of references 7 inproceedings 2024_lohmoeller_scematch 2024 12 17 2123-2132 Advances in single-cell RNA sequencing (scRNA-seq) have dramatically enhanced our understanding of cellular functions and disease mechanisms. Despite its potential, scRNA-seq faces significant challenges related to data privacy, cost, and Intellectual Property (IP) protection, which hinder the sharing and collaborative use of these sensitive datasets. In this paper, we introduce a novel method, scE(match), a privacy-preserving tool that facilitates the matching of single-cell clusters between different datasets by relying on scmap as an established projection tool, but without compromising data privacy or IP. scE(match) utilizes homomorphic encryption to ensure that data and unique cell clusters remain confidential while enabling the identification of overlapping cell types for further collaboration and downstream analysis. Our evaluation shows that scE(match) performantly matches cell types across datasets with high precision, addressing both practical and ethical concerns in sharing scRNA-seq data. This approach not only supports secure data collaboration but also fosters advances in biomedical research by reliably protecting sensitive information and IP rights. confidentiality; scmap; privacy-preserving computations; offloading; healthcare rfc;health https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-lohmoeller-scEmatch.pdf IEEE Proceedings of the 23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom '24), December 17-21, 2024, Sanya, China Sanya, China TrustCom 2024 December 17-21, 2024 accepted en 979-8-3315-0620-9 2324-9013 10.1109/TrustCom63139.2024.00294 1 JohannesLohmöller JannisScheiber RafaelKramann KlausWehrle SikanderHayat JanPennekamp article 2024_welten_pasta arXiv 2024 12 2 With the introduction of data protection regulations, the need for innovative privacy-preserving approaches to process and analyse sensitive data has become apparent. One approach is the Personal Health Train (PHT) that brings analysis code to the data and conducts the data processing at the data premises. However, despite its demonstrated success in various studies, the execution of external code in sensitive environments, such as hospitals, introduces new research challenges because the interactions of the code with sensitive data are often incomprehensible and lack transparency. These interactions raise concerns about potential effects on the data and increases the risk of data breaches. To address this issue, this work discusses a PHT-aligned security and audit pipeline inspired by DevSecOps principles. The automated pipeline incorporates multiple phases that detect vulnerabilities. To thoroughly study its versatility, we evaluate this pipeline in two ways. First, we deliberately introduce vulnerabilities into a PHT. Second, we apply our pipeline to five real-world PHTs, which have been utilised in real-world studies, to audit them for potential vulnerabilities. Our evaluation demonstrates that our designed pipeline successfully identifies potential vulnerabilities and can be applied to real-world studies. In compliance with the requirements of the GDPR for data management, documentation, and protection, our automated approach supports researchers using in their data-intensive work and reduces manual overhead. It can be used as a decision-making tool to assess and document potential vulnerabilities in code for data processing. Ultimately, our work contributes to an increased security and overall transparency of data processing activities within the PHT framework. health 10.48550/arXiv.2412.01275 SaschaWelten KarlKindermann AhmetPolat MartinGörz MaximilianJugl LaurenzNeumann AlexanderNeumann JohannesLohmöller JanPennekamp StefanDecker article 2024_querfurth_mcbert bioRxiv 2024 11 7 health; rfc 10.1101/2024.11.04.621897 Benediktvon Querfurth JohannesLohmöller JanPennekamp ToreBleckwehl RafaelKramann KlausWehrle SikanderHayat inproceedings 2024-wagner-madtls 2024 7 1 https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-wagner-madtls.pdf ACM 19th ACM ASIA Conference on Computer and Communications Security (ACM AsiaCCS '24), Singapur Singapur ACM ASIA Conference on Computer and Communications Security (AsiaCCS) July 1-5, 2024 10.1145/3634737.3637640 1 EricWagner DavidHeye MartinSerror IkeKunze KlausWehrle MartinHenze inproceedings 2024-kunze-spintrap 2024 5 7 legato https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-kunze-spintrap.pdf IEEE/IFIP Proceedings of the 2024 IEEE/IFIP Network Operations and Management Symposium (NOMS '24) 2024 IEEE/IFIP Network Operations and Management Symposium 10.1109/NOMS59830.2024.10575719 1 IkeKunze ConstantinSander LarsTissen BenediktBode KlausWehrle inproceedings 2024-kunze-civic 2024 5 internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-kunze-civic.pdf IEEE Proceedings of the 7th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS '24) 10.1109/ICPS59941.2024.10639999 1 IkeKunze DominikScheurenberg LiamTirpitz SandraGeisler KlausWehrle article 2024_pennekamp_supply-chain-survey ACM Computing Surveys 2024 2 1 56 2 Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010--2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points. information flows; data communication; supply chain management; data security; data sharing; systematic literature review internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2024/2024-pennekamp-supply-chain-survey.pdf ACM 0360-0300 10.1145/3606693 1 JanPennekamp RomanMatzutt ChristopherKlinkmüller LennartBader MartinSerror EricWagner SidraMalik MariaSpiß JessicaRahn TanGürpinar EduardVlad Sander J. J.Leemans Salil S.Kanhere VolkerStich KlausWehrle