What’s Covered?
The report starts by framing the growing tension between AI’s hunger for data and the need to protect individual rights. It makes the case that PETs can unlock responsible data sharing across sectors, while also supporting compliance, trust, and innovation. It covers six PET families in depth—synthetic data, homomorphic encryption, differential privacy, federated learning, trusted execution environments, and secure multi-party computation. Each section outlines how these tools apply across the AI lifecycle: from data sourcing to training, validation, deployment, and collaboration.
Across 19 detailed case studies, the report illustrates how PETs are already used in contexts like cancer research, photo libraries, customer service, and biometric payments. It also flags key technical and governance challenges, such as re-identification risks, performance trade-offs, compute intensity, and lack of standards. The final chapters offer future-facing guidance: urging regulators to offer practical clarity, advocating for risk-based anonymization thresholds, and pushing industry to invest in education, tooling, and regulatory sandboxes. The broader message is clear: PETs aren’t a fix-all, but they are vital infrastructure for enabling privacy by design in AI.
The report’s contents include:
1. Introduction
2. Applications for PETs in AI (broken down by technology and use cases)
3. Privacy and Utility
4. Broader Advantages of PETs in AI
5. Additional Considerations and Recommendations
6. Endnotes
Why it matters?
This paper doesn’t just describe PETs—it shows how they can be made useful. It brings much-needed structure to conversations around data governance, offering regulators and AI developers practical next steps to move privacy from compliance checkbox to operational strategy. PETs are framed not as privacy barriers, but as enablers of better, fairer AI.
What’s Missing?
While the report gives great technical depth, some parts remain high-level when it comes to implementation. There’s limited analysis of cost, integration challenges, or how SMEs without deep privacy engineering teams can access these tools. It also doesn’t fully address overlaps with cybersecurity frameworks or how PETs intersect with broader risk governance models (like NIST RMF or ISO 42001). More hands-on deployment patterns or comparative performance data would help decision-makers understand trade-offs better.
Best For:
This paper is a goldmine for legal teams, AI ethics leads, data scientists, and policy designers. It’s especially useful for regulators, civil society groups, and privacy engineers shaping AI assurance schemes or designing sandboxes for responsible innovation.
Source Details:
Privacy-Enhancing and Privacy-Preserving Technologies in AI: Enabling Data Use and Operationalizing Privacy by Design and Default. Centre for Information Policy Leadership (CIPL), March 2025.
The report draws on case studies and insights from members like Google, IBM, Meta, Mastercard, and others. CIPL is a global privacy think tank housed within Hunton Andrews Kurth LLP. It facilitates dialogue between companies, regulators, and researchers. The report reflects collective input from CIPL’s advisory network but does not represent legal advice or the views of any single company. With its access to regulatory and industry leaders, CIPL is positioned to surface pragmatic strategies and recommendations that can help turn privacy theory into working AI governance systems.
