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S2E8: Leveraging Federated Learning for Input Privacy with Victor Platt Tijd: 39:41
Victor Platt is a Senior AI Security and Privacy Strategist who previously served as Head of Security and Privacy for privacy tech company, Integrate.ai. Victor was formerly a founding member of the Risk AI Team with Omnia AI, Deloitt’s artificial intelligence practice in Canada. He joins today to discuss privacy enhancing technologies (PETs) that are shaping industries around the world, with a focus on federated learning.
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Victor views PETs as functional requirements and says they shouldn’t be buried in your design document as nonfunctional obligations. In his work, he has found key gaps where organizations were only doing “security for security’s sake.” Rather, he believes organizations should be thinking about it at the forefront. Not only that, we should all be getting excited about it because we all have a stake in privacy.
With federated learning, you have the tools available to train ML models on large data sets with precision at scale without risking user privacy. In this conversation, Victor demystifies what federated learning is, describes the 2 different types: at the edge and across data silos, and explains how it works and how it compares to traditional machine learning.We deep dive into how an organization knows when to use federated learning, with specific advice for developers and data scientists as they implement it into their organizations.
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Listen to the episode on Apple Podcasts, Spotify, iHeartRadio, or on your favorite podcast platform.
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Topics Covered:
What 'federated learning' is and how it compares to traditional machine learningWhen an organization should use vertical federated learning vs horizontal federated learning, or instead a hybrid versionA key challenge in 'transfer learning': knowing whether two data sets are related to each other and techniques to overcome this, like 'private set intersection'How the future of technology will be underpinned by a 'constellation of PETs' The distinction between 'input privacy' vs. 'output privacy'Different kinds of federated learning with use case examplesWhere the responsibility for adding PETs lies within an organizationThe key barriers to adopting federated learning and other PETs within different industries and use casesHow to move the needle on data privacy when it comes to legislation and regulationResources Mentioned:
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Releasedatum: 28-2-2023 15:00:00
