This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-09-08 Creation time: 01-19-08 --- Number of references 10 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-mobiquitous-comparison 2017 11 7 543-544 trinics https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-henze-mobiquitous-comparison.pdf Online ACM Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) - Poster Session, Melbourne, VIC, Australia en 978-1-4503-5368-7 10.1145/3144457.3144511 1 MartinHenze RitsumaInaba Ina BereniceFink Jan HenrikZiegeldorf inproceedings 2017-henze-mobiquitous-cloudanalyzer 2017 11 7 262-271 Developers of smartphone apps increasingly rely on cloud services for ready-made functionalities, e.g., to track app usage, to store data, or to integrate social networks. At the same time, mobile apps have access to various private information, ranging from users' contact lists to their precise locations. As a result, app deployment models and data flows have become too complex and entangled for users to understand. We present CloudAnalyzer, a transparency technology that reveals the cloud usage of smartphone apps and hence provides users with the means to reclaim informational self-determination. We apply CloudAnalyzer to study the cloud exposure of 29 volunteers over the course of 19 days. In addition, we analyze the cloud usage of the 5000 most accessed mobile websites as well as 500 popular apps from five different countries. Our results reveal an excessive exposure to cloud services: 90 % of apps use cloud services and 36 % of apps used by volunteers solely communicate with cloud services. Given the information provided by CloudAnalyzer, users can critically review the cloud usage of their apps. Privacy; Smartphones; Cloud Computing; Traffic Analysis trinics https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-henze-mobiquitous-cloudanalyzer.pdf Online ACM Proceedings of the 14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous '17), November 7-10, 2017, Melbourne, VIC, Australia Melbourne, VIC, Australia November 7-10, 2017 en 978-1-4503-5368-7 10.1145/3144457.3144471 1 MartinHenze JanPennekamp DavidHellmanns ErikMühmer Jan HenrikZiegeldorf ArthurDrichel 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-zimmermann-secon 2017 6 12 maki IEEE 14th IEEE International Conference on Sensing, Communication, and Networking (SECON 2017), San Diego, USA San Diego, USA 14th IEEE International Conference on Sensing, Communication, and Networking (SECON 2017) 12.06.2017 - 14.06.2017 en 978-1-5090-6599-8 10.1109/SAHCN.2017.7964939 1 TorstenZimmermann HannoWirtz Jan HenrikZiegeldorf ChristianSteinhaus 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-ziegeldorf-codaspy-priward 2017 3 22 83-94 mynedata https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-ziegeldorf-codaspy-priward.pdf Online ACM Proceedings of the 7th ACM Conference on Data and Application Security and Privacy (CODASPY 2017), Scottsdale, AZ, USA en 978-1-4503-4523-1 10.1145/3029806.3029816 1 Jan HenrikZiegeldorf JanMetzke JanRüth MartinHenze KlausWehrle inproceedings 2017-ziegeldorf-wons-tracemixer 2017 2 21 17-24 mynedata https://www.comsys.rwth-aachen.de/fileadmin/papers/2017/2017-ziegeldorf-wons-tracemixer.pdf Online IEEE Proceedings of the 2017 13th Annual Conference on Wireless On-demand Network Systems and Services (WONS), Jackson Hole, WY, USA en 978-3-901882-88-3 10.1109/WONS.2017.7888771 1 Jan HenrikZiegeldorf MartinHenze JensBavendiek 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 phdthesis 2017-ziegeldorf-phdthesis 2017 RWTH Aachen University Jan HenrikZiegeldorf