Protecting Patients: Privacy-Preserving Computing in Patient Data
May 13, 2026•Channel
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Video Details
Published2 months ago
Duration34:38
Video IDTJvkm2iUeDM
Languageen
CategoryEducation
PrivacyPublic
Made for KidsNo
Video TypeRegular Video
Performance Metrics
Views269
Likes6
Comments0
Engagement Rate2.23%
Likes per 100 views2.23
Comments per 1K views0.00
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Description
A version of this video with audio description track is available here:
https://www.youtube.com/watch?v=vMhp-OY5ZQ8
Privacy-preserving computation can help hospitals and researchers use sensitive health data without exposing it. Farinaz Koushanfar, Ph.D., UC San Diego, explains how secure computation and distributed learning make it possible to collaborate on medical data while protecting patient privacy. Koushanfar examines secure multi-party computation, zero-knowledge proofs, and federated and split learning, helping clarify how health systems can work together despite data silos, incompatibility, security threats, and re-identification risk. This work helps explain how medical AI can learn from private data more safely and points toward more secure, robust, and trustworthy healthcare systems. Recorded on 01/30/2026. [5/2026] [Show ID: 41367]
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