Episode 395: Katharine Jarmul on Security and Privacy in Machine Learning

Filed in Episodes by on January 10, 2020 0 Comments

Katharine Jarmul of DropoutLabs discusses security and privacy concerns as they relate to Machine Learning. Host Justin Beyer spoke with Jarmul about attacks that can be leveraged against data pipelines and machine learning models; attack types – adversarial example, model inference, deanonymization; and how they can be utilized to manipulate model outcomes; the dangers of Machine Learning as a Service (MLaaS) platforms; privacy concerns surrounding the use and collection of data; securing data and APIs; Privacy Preserving Machine Learning: Federated Learning, and Encrypted Learning through techniques such as Homomorphic Encryption and Secure Multi-Party Computation.

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SE Radio theme music: “Broken Reality” by Kevin MacLeod (incompetech.com)
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