Deploying Machine Learning Solutions That Preserve Privacy and Secure Data

Deploying Machine Learning Solutions that Preserve Privacy and Secure Data

May 20, 2021


As converged security teams expand the practice of processing multiple data streams to deliver more actionable security insights, they must ensure the security and privacy of these robust data streams. Federated learning is a distributed machine learning approach that enables organizations to collaborate on data projects without sharing sensitive information, such as patient records, financial transactions, or protected secrets.

In this one hour webcast, brought to you by the SIA Cybersecurity Advisory Board, Jason Martin, Principal Engineer in the Security Solutions Lab and Manager of the Secure Intelligence Team at Intel Labs, will present how Federated Learning can help solve some of the security integration challenges of the future and addresses the cybersecurity and privacy issues inherent with other forms of machine learning. Specifically, attendees will learn:

  • How protecting data models leads to successful proactive security solutions
  • What Federated Learning is, and its applications in converged security
  • Why hardware-based security solutions may be the key to a successful federated learning solution

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