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@Inproceedings { 2019-dahlmanns-icnp-knowledgeSystem,
   title = {Privacy-Preserving Remote Knowledge System},
   year = {2019},
   month = {10},
   day = {7},
   abstract = {More and more traditional services, such as malware detectors or collaboration services in industrial scenarios, move to the cloud. However, this behavior poses a risk for the privacy of clients since these services are able to generate profiles containing very sensitive information, e.g., vulnerability information or collaboration partners. Hence, a rising need for protocols that enable clients to obtain knowledge without revealing their requests exists. To address this issue, we propose a protocol that enables clients (i) to query large cloud-based knowledge systems in a privacy-preserving manner using Private Set Intersection and (ii) to subsequently obtain individual knowledge items without leaking the client’s requests via few Oblivious Transfers. With our preliminary design, we allow clients to save a significant amount of time in comparison to performing Oblivious Transfers only.},
   note = {Poster Session},
   keywords = {private query protocol; knowledge system; remote knowledge; private set intersection; oblivious  transfer},
   tags = {kimusin; internet-of-production},
   url = {https://www.comsys.rwth-aachen.de/fileadmin/papers/2019/2019-dahlmanns-knowledge-system.pdf},
   publisher = {IEEE},
   booktitle = {Proceedings of the 27th IEEE International Conference on Network Protocols (ICNP '19), October 7-10, 2019, Chicago, IL, USA},
   event_place = {Chicago, IL, USA},
   event_name = {27th IEEE International Conference on Network Protocols (ICNP 2019)},
   event_date = {7-10. Oct. 2019},
   ISBN = {978-1-7281-2700-2},
   ISSN = {2643-3303},
   DOI = {10.1109/ICNP.2019.8888121},
   reviewed = {1},
   author = {Dahlmanns, Markus and Dax, Chris and Matzutt, Roman and Pennekamp, Jan and Hiller, Jens and Wehrle, Klaus}
}