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Explore the critical privacy considerations in production machine learning environments during this 32-minute conference talk from MLOps World. Delve into the challenges posed by adversaries who can exploit weaknesses in machine learning models to breach user data privacy or steal valuable intellectual property. Gain insights into state-of-the-art research at the intersection of privacy and machine learning, including methods to estimate and defend against privacy leakage. Learn about secure collaboration techniques that protect privacy when multiple parties wish to collectively improve their algorithms. Discover the emerging field of Machine Unlearning and its implications for complying with data protection regulations like GDPR. Benefit from the expertise of Christopher A. Choquette Choo, a Machine Learning Researcher at Google with extensive experience in adversarial machine learning, federated learning, and differential privacy.
