Fabian Boemer
Fabian Boemer
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nGraph-HE2: A high-throughput framework for neural network inference on encrypted data
F Boemer, A Costache, R Cammarota, C Wierzynski
Proceedings of the 7th ACM Workshop on Encrypted Computing & Applied …, 2019
nGraph-HE: a graph compiler for deep learning on homomorphically encrypted data
F Boemer, Y Lao, R Cammarota, C Wierzynski
Proceedings of the 16th ACM International Conference on Computing Frontiers …, 2019
MP2ML: A mixed-protocol machine learning framework for private inference
F Boemer, R Cammarota, D Demmler, T Schneider, H Yalame
Proceedings of the 15th International Conference on Availability …, 2020
Parameter-free image segmentation with SLIC
F Boemer, E Ratner, A Lendasse
Neurocomputing 277, 228-236, 2018
Intel HEXL: accelerating homomorphic encryption with intel AVX512-IFMA52
F Boemer, S Kim, G Seifu, F DM de Souza, V Gopal
Proceedings of the 9th on Workshop on Encrypted Computing & Applied …, 2021
Enabling homomorphically encrypted inference for large dnn models
G Lloret-Talavera, M Jorda, H Servat, F Boemer, C Chauhan, ...
IEEE Transactions on Computers 71 (5), 1145-1155, 2021
Trustworthy ai inference systems: An industry research view
R Cammarota, M Schunter, A Rajan, F Boemer, Á Kiss, A Treiber, ...
arXiv preprint arXiv:2008.04449, 2020
Accelerating encrypted computing on intel gpus
Y Zhai, M Ibrahim, Y Qiu, F Boemer, Z Chen, A Titov, A Lyashevsky
2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2022
Systems, methods, apparatus and articles of manufacture to prevent unauthorized release of information associated with a function as a service
R Cammarota, F Boemer, CM Wierzynski, A Rajan, R Misoczki
US Patent App. 16/910,958, 2020
Developing privacy-preserving AI systems: The lessons learned
H Chen, SU Hussain, F Boemer, E Stapf, AR Sadeghi, F Koushanfar, ...
2020 57th ACM/IEEE Design Automation Conference (DAC), 1-4, 2020
Processor with private pipeline
C Wierzynski, F Boemer, R Cammarota
US Patent App. 16/585,856, 2021
Journal: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, 2020
F Boemer, R Cammarota, D Demmler, T Schneider, H Yalame
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