Protecting Sensitive Data using Differential Privacy

From the creator

Using data for machine learning and analytics can potentially expose private data. How can we leverage data while ensuring that private information remains private? In this session, we'll discuss how differential privacy can be used to preserve privacy. We'll demonstrate how you can use newly released open source system, WhiteNoise, to put DP into your applications. Learn More: Whitenoise Paper aka.ms/whitenoisewhitepaper Open Differential Privacy https://aka.ms/OpenDifferentialPrivacy Azure Machine Learning How To https://aka.ms/AMLHowTo Follow: https://twitter.com/slbird The AI Show's Favorite links: Don't miss new episodes, subscribe to the AI Show: https://aka.ms/aishowsubscribe Create a Free account (Azure): https://aka.ms/aishow-seth-azurefree Deep Learning vs. Machine Learning: https://aka.ms/deeplearningmachinelea... Get Started with Machine Learning: https://aka.ms/GetStartedWithMachineL... Follow: https://twitter.com/sethjuarez Follow: https://twitter.com/ch9 Follow: https://twitter.com/Azure Follow: https://twitter.com/msdev

Published 2020-05-18 · Watch on YouTube ↗
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