Privacy and security in machine learning: attacks and defenses - Josep Domingo Ferrer
In this talk, I will review privacy and security attacks against conventional machine learning, and I will discuss defenses and the conflict between defending privacy and security in decentralized ML. The usefulness of differential privacy as a privacy defense will be examined. I will also touch on some myths regarding privacy attacks against conventional ML. Some hints on how all this can apply to generative ML will be given as well
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