GDPR, AI & use of Video data
summary
Claims that camera data can be made useful for ML while protecting identity by two concrete tools: precision blur that makes faces undetectable and deep natural anonymization that replaces each face with a non-reversible synthetic face so automated matching fails. Cites IDC forecast of ~USD 100B AI spend by 2023, shows demos and case studies where verification confidence drops to non-match, notes deployments with automotive clients and Deutsche Bahn, admits no universal GDPR stamp yet and ongoing independent audits and limits (gait re‑identification still under study).
AI-written summary · about
chapters (5)
related talks
21mCombining Privacy and AI
20mWIP: Blurred Lines -- A GDPR-Compliant Framework for Anonymising Automotive...
01h 19mAligning Data Privacy and AI Governance
33mMachine Learning in the age of increasing Data Privacy Consciousness
01h 11mLiquid AI: Accountability and governance of transnational AI systems.
01h 09mGOVERNANCE AND REGULATION OF AI FROM THE PERSPECTIVE OF AUTONOMY AND PRIVACY
01h 15mHOW TO RECONCILE FACIAL RECOGNITION TECHNOLOGIES WITH CONSUMERS' PRIVACY
32mPrivacy Protection. Privacy by Design: Strengthening Compliance