WebHossein Yalame. Technical University of Darmstadt. Verified email at encrypto.cs.tu-darmstadt.de ... T Schneider, M Stillger, A Wigandt, H Yalame. Cryptology ePrint … WebFeb 20, 2024 · Hossein Yalame Published 20 February 2024 Computer Science ArXiv As a distributed machine learning paradigm, federated learning (FL) conveys a sense of privacy to contributing participants because training data never leaves their devices. However, gradient updates and the aggregated model still reveal sensitive information.
Hossein Yalame (@HYalame) Twitter
WebHossein Yalame TU Darmstadt Abstract Secure Multi-party Computation (MPC) allows a set of mutually distrusting parties to jointly evaluate a function on their private inputs while maintaining input privacy. In this work, we improve semi-honest secure two-party computation (2PC) over rings, with a focus on the efficiency of the online phase. Web@inproceedings {280048, author = {Thien Duc Nguyen and Phillip Rieger and Huili Chen and Hossein Yalame and Helen M{\"o}llering and Hossein Fereidooni and Samuel … buffalo skull with horns
SAFELearn: Secure Aggregation for private FEderated Learning
WebAssociate Professor. Department of Software Engineering. Lakehead University. 955 Oliver Road, Thunder Bay, Ontario, Canada, P7B 5E1. email:[email protected] WebJan 12, 2024 · BibTeX Copy to clipboard. @misc{cryptoeprint:2024/025, author = {Thien Duc Nguyen and Phillip Rieger and Huili Chen and Hossein Yalame and Helen Möllering and … WebJun 11, 2024 · In this paper, we propose the first secure aggregation protocol that considers users as potentially malicious. This new protocol enables the correct computation of the aggregate result, in a privacy preserving manner, only if individual inputs belong to a legitimate interval. crm software pc magazine