This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-07-04 Creation time: 05-27-53 --- Number of references 5 incollection 2017-cps-henze-network 2017 11 13 25-56 sensorcloud,ipacs Song, Houbing and Fink, Glenn A. and Jeschke, Sabina Wiley-IEEE Press First 2 Security and Privacy in Cyber-Physical Systems: Foundations, Principles and Applications en 978-1-119-22604-8 10.1002/9781119226079.ch2 1 MartinHenze JensHiller RenéHummen RomanMatzutt KlausWehrle Jan HenrikZiegeldorf inproceedings 2017-henze-trustcom-dcam 2017 8 1 185-192 sscilops, ipacs https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-henze-trustcom-dcam.pdf Online IEEE Proceedings of the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom), Sydney, NSW, Australia en 978-1-5090-4905-9 2324-9013 10.1109/Trustcom/BigDataSE/ICESS.2017.236 1 MartinHenze BenediktWolters RomanMatzutt TorstenZimmermann KlausWehrle article 2017-ziegeldorf-bmcmedgenomics-bloom BMC Medical Genomics 2017 7 26 10 Suppl 2 29-42 Whole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations. We propose FHE-Bloom and PHE-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. FHE-Bloom is fully secure in the semi-honest model while PHE-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance. We implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while PHE-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries. Both approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, FHE-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, PHE-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude. Proceedings of the 5th iDASH Privacy and Security Workshop 2016 Secure outsourcing; Homomorphic encryption; Bloom filters sscilops; mynedata; rfc https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-ziegeldorf-bmcmedgenomics-bloom.pdf Online BioMed Central Chicago, IL, USA November 11, 2016 en 1755-8794 10.1186/s12920-017-0277-y 1 Jan HenrikZiegeldorf JanPennekamp DavidHellmanns FelixSchwinger IkeKunze MartinHenze JensHiller RomanMatzutt KlausWehrle inproceedings 2017-henze-ic2e-prada 2017 4 4 252-258 ssiclops, ipacs https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-henze-ic2e-prada.pdf Online IEEE Proceedings of the 2017 IEEE International Conference on Cloud Engineering (IC2E 2017), Vancouver, BC, Canada en 978-1-5090-5817-4 10.1109/IC2E.2017.32 1 MartinHenze RomanMatzutt JensHiller ErikMühmer Jan HenrikZiegeldorf Johannesvan der Giet KlausWehrle inproceedings 2017-matzutt-mynedata 2017 1073-1084 Personal user data is collected and processed at large scale by a handful of big providers of Internet services. This is detrimental to users, who often do not understand the privacy implications of this data collection, as well as to small parties interested in gaining insights from this data pool, e.g., research groups or small and middle-sized enterprises. To remedy this situation, we propose a transparent and user-controlled data market in which users can directly and consensually share their personal data with interested parties for monetary compensation. We define a simple model for such an ecosystem and identify pressing challenges arising within this model with respect to the user and data processor demands, legal obligations, and technological limits. We propose myneData as a conceptual architecture for a trusted online platform to overcome these challenges. Our work provides an initial investigation of the resulting myneData ecosystem as a foundation to subsequently realize our envisioned data market via the myneData platform. Presentation slides are in German Personal User Data, Personal Information Management, Data Protection Laws, Privacy Enhancing Technologies, Platform Design, Profiling mynedata_show https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-matzutt-informatik-mynedata.pdf https://www.comsys.rwth-aachen.de/fileadmin/misc/mynedata/talks/2017-matzutt-informatik-mynedata-presentation.pdf Presentation slides Eibl, Maximilian and Gaedke, Martin Gesellschaft für Informatik, Bonn INFORMATIK 2017 Chemnitz INFORMATIK 2017 2017-09-28 English 978-3-88579-669-5 1617-5468 10.18420/in2017_109 1 RomanMatzutt DirkMüllmann Eva-MariaZeissig ChristianeHorst KaiKasugai SeanLidynia SimonWieninger Jan HenrikZiegeldorf GerhardGudergan IndraSpiecker gen. Döhmann KlausWehrle MartinaZiefle