2/ Morten described how @tf_encrypted began, the current state of the project, and how it fits into the broader space of privacy-preserving analytics and machine learning. He notes that current solutions are still too slow for many (near) real-time applications.
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3/ It’s clear that privacy-preserving ML solutions will employ a variety of techniques including cryptography, homomorphic encryption, federated learning, secure aggregation, differential privacy, MPC and more. We discussed these &
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4/ If you are needing a great intro to privacy-preserving analytics and
#MachineLearning, I recommend you listen to my conversation with@mortendahlcs. If you have feedback or suggestions for us, fill out the contact form on#TheDataExchangePod site /Endhttps://thedataexchange.media/the-state-of-privacy-preserving-machine-learning …Prikaži ovu nit
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