Machine learning applied to healthcare or otherwise sensitive data may be blocked if privacy isn’t adequately addressed. Morten Dahl reviews modern cryptographic techniques such as homomorphic encryption and multiparty computation, sharing concrete examples in TensorFlow using the open source library TF Encrypted. You’ll discover how to make predictions without exposing the prediction input and how to fit a model without ever exposing the training data. Join in to learn how to get started with privacy-preserving techniques today, without needing to master the cryptography.
Morten Dahl is cofounder and research scientist at Dropout Labs, a startup building a platform for secure, privacy-preserving machine learning to manage the sensitive, competitive, and regulatory nature of data. He is also lead developer on TF Encrypted, an open source project for integrating and experimenting with privacy-preserving machine learning directly in TensorFlow. With a background in cryptography and privacy, Morten has spent recent years applying and adapting techniques from these fields to machine learning, focusing on practical tools and concrete applications in hope of making the field more accessible to practitioners.
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