This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-09-08 Creation time: 01-47-08 --- Number of references 7 conference 2017-fink-brainlab-gmds 2017 8 29 Best Abstract Award https://www.egms.de/static/en/meetings/gmds2017/17gmds137.shtml 06.09.19 German Medical Science GMS Publishing House (2017) 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg GMDS 2017 17-21 September 2017 10.3205/17gmds137 1 Ina BereniceFink BerndHankammer ThomasStopinski YannicTitgemeyer RoannRamos EkaterinaKutafina Jó AgilaBitsch Stephan MichaelJonas proceedings 2017-SymPerfPoster 2017 8 21 spp,erc,symbiosys,reflexes https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-rath-sym-perf-poster.pdf ACM Los Angeles, USA ACM SIGCOMM 2017 Poster 21.8.2017 - 25.8.2017 en 978-1-4503-5057-0/17/08 10.1145/3123878.3131977 1 FelixRath JohannesKrude JanRüth DanielSchemmel OliverHohlfeld Jó AgilaBitsch Link 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 article dombrowski-vdi VDE Positionspapier 2017 6 1 VDE - Verband der Elektrotechnik, Elektronik, Informationstechnik e.V.

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IsmetAktas AlexanderBentkus FlorianBonanati ArminDekorsy ChristianDombrowski MichaelDoubrava AliGolestani FrankHofmann MikeHeidrich StefanHiensch RüdigerKays MichaelMeyer AndreasMüller Stephanten Brink NedaPetreska MilanPopovic LutzRauchhaupt AhmadSaad HansSchotten ChristophWöste IngoWolff inproceedings 2017-serror-pads-cows 2017 5 24 memosim,symbiosys https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-serror-pads-cows.pdf ACM online Proceedings of the 5th ACM SIGSIM/PADS Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS’17), Singapore, Singapore Singapore, Singapore 5th ACM SIGSIM/PADS Conference on Principles of Advanced Discrete Simulation (SIGSIM-PADS’17) May 24-26, 2017 978-1-4503-4489-0 10.1145/3064911.3064913 1 MartinSerror Jörg ChristianKirchhof MirkoStoffers KlausWehrle JamesGross conference 2017-fink-brainlab 2017 4 24 2 /fileadmin/papers/2017/2017-fink-brainlab.pdf http://informaticsforhealth.org/wp-content/uploads/2017/04/IFH2017-Digital-Programme.pdf 2017-05-09 Online Informatics for Health 2017, Manchester UK Manchester, UK Informatics for Health 2017, Manchester UK 24-26 April 2017 en 1 Ina BereniceFink BerndHankammer ThomasStopinsky RoannRamos EkaterinaKutafina Jó AgilaBitsch Link StephanJonas 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