KEY LEARNINGS
- AI privacy risks extend beyond data theft to include 'inference risks,' where sensitive details are deduced from public data.
- The principle of data minimization conflicts with modern AI's hunger for massive training datasets.
- Re-identification attacks can reveal individual identities even within datasets that have been anonymized.
- Privacy-Enhancing Technologies (PETs) like differential privacy allow organizations to learn from data without exposing individuals.
- Governance requires shifting from 'notice and consent' models to proactive Privacy by Design frameworks.
- 🌐NIST Privacy Engineering ProgramNIST resources for privacy engineering.
- 📄The Algorithmic Foundations of Differential PrivacyTechnical foundation of differential privacy.
- 🌐IAPP: AI Governance Professional Body of KnowledgeProfessional certification for AI governance.
- Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy.
- Hill, K. (2020). The Secretive Company That Might End Privacy as We Know It. The New York Times.
- McMahan, B., et al. (2017). Communication-Efficient Learning of Deep Networks from Decentralized Data.
