Privacy was once again a major theme of mainstream media coverage of tech. The Great Hack made waves after its Netflix release, @shoshanazuboff released a monumental work on Surveillance Capitalism, and Facebook announced a new privacy-first vision for social media.
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Exciting progress has been made: encrypted inference of modern neural networks has become practical using both multi-party computation and secure enclaves, and new variants of differential privacy aim to make privacy practical. https://arxiv.org/abs/1905.02383
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One new trend in research we’re particularly excited about is the development of new ML techniques that are privacy-oriented. If you’re a researcher in this line of work, we encourage you to submit to the ICLR Trustworthy ML Workshop by Jan 31! https://trustworthyiclr20.github.io/
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The gap between research and practice of PPML is as wide as ever, however applications of the tech beyond Google and Apple have shown up for the first time. We’re excited to see what new announcements 2020 will bring about for PPML adoption. https://news.developer.nvidia.com/first-privacy-preserving-federated-learning-system/ …
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Tons of new open-source code!
We got TF Federated and CrypTen, a variety of new packages within @tf_encrypted, an acceleration of grant-driven development within@openminedorg, and a host of new projects. Check the year-end review for more!https://medium.com/dropoutlabs/bridging-microsoft-seal-into-tensorflow-b04cc2761ad4 …Prikaži ovu nit -
Here’s to another fast-paced year for PPML in 2020


With a focus on open science and further collaboration between researchers and practitioners, we’re looking forward to engineering security and privacy into the ML systems of today & tomorrow!https://medium.com/dropoutlabs/privacy-preserving-machine-learning-2019-a-year-in-review-123733e61705 …Prikaži ovu nit
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