Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Ryffel, Theo"'
We analyse the privacy leakage of noisy stochastic gradient descent by modeling R\'enyi divergence dynamics with Langevin diffusions. Inspired by recent work on non-stochastic algorithms, we derive similar desirable properties in the stochastic setti
Externí odkaz:
http://arxiv.org/abs/2201.11980
Autor:
Hall, Adam James, Jay, Madhava, Cebere, Tudor, Cebere, Bogdan, van der Veen, Koen Lennart, Muraru, George, Xu, Tongye, Cason, Patrick, Abramson, William, Benaissa, Ayoub, Shah, Chinmay, Aboudib, Alan, Ryffel, Théo, Prakash, Kritika, Titcombe, Tom, Khare, Varun Kumar, Shang, Maddie, Junior, Ionesio, Gupta, Animesh, Paumier, Jason, Kang, Nahua, Manannikov, Vova, Trask, Andrew
We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems. This framework is demonstrated through the design and implementation of
Externí odkaz:
http://arxiv.org/abs/2104.12385
Autor:
Ziller, Alexander, Passerat-Palmbach, Jonathan, Ryffel, Théo, Usynin, Dmitrii, Trask, Andrew, Junior, Ionésio Da Lima Costa, Mancuso, Jason, Makowski, Marcus, Rueckert, Daniel, Braren, Rickmer, Kaissis, Georgios
The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains. The conflict between data usage and privacy protection requirements in such systems must be resolved for optimal res
Externí odkaz:
http://arxiv.org/abs/2012.06354
We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data. Our semi-honest 2-party computation protocol (with a trusted dealer) leverages function secret sharing, a recent li
Externí odkaz:
http://arxiv.org/abs/2006.04593
Autor:
Brundage, Miles, Avin, Shahar, Wang, Jasmine, Belfield, Haydn, Krueger, Gretchen, Hadfield, Gillian, Khlaaf, Heidy, Yang, Jingying, Toner, Helen, Fong, Ruth, Maharaj, Tegan, Koh, Pang Wei, Hooker, Sara, Leung, Jade, Trask, Andrew, Bluemke, Emma, Lebensold, Jonathan, O'Keefe, Cullen, Koren, Mark, Ryffel, Théo, Rubinovitz, JB, Besiroglu, Tamay, Carugati, Federica, Clark, Jack, Eckersley, Peter, de Haas, Sarah, Johnson, Maritza, Laurie, Ben, Ingerman, Alex, Krawczuk, Igor, Askell, Amanda, Cammarota, Rosario, Lohn, Andrew, Krueger, David, Stix, Charlotte, Henderson, Peter, Graham, Logan, Prunkl, Carina, Martin, Bianca, Seger, Elizabeth, Zilberman, Noa, hÉigeartaigh, Seán Ó, Kroeger, Frens, Sastry, Girish, Kagan, Rebecca, Weller, Adrian, Tse, Brian, Barnes, Elizabeth, Dafoe, Allan, Scharre, Paul, Herbert-Voss, Ariel, Rasser, Martijn, Sodhani, Shagun, Flynn, Carrick, Gilbert, Thomas Krendl, Dyer, Lisa, Khan, Saif, Bengio, Yoshua, Anderljung, Markus
With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations and norms in industry and academia are insufficient to ensure responsibl
Externí odkaz:
http://arxiv.org/abs/2004.07213
Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sens
Externí odkaz:
http://arxiv.org/abs/1905.10214
Autor:
Ryffel, Theo, Trask, Andrew, Dahl, Morten, Wagner, Bobby, Mancuso, Jason, Rueckert, Daniel, Passerat-Palmbach, Jonathan
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abst
Externí odkaz:
http://arxiv.org/abs/1811.04017
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.