
Data Privacy & Privacy Enhancing Technologies
Description
Explores the rapid commercialization of PETs to balance data utility with compliance. Evaluates emerging solutions in federated AI training, zero-knowledge proofs, and secure multi-party computation. Provides a market valuation through 2030 and benchmarks vendor offerings across Europe, North America, and APAC. Emphasizes the integration of privacy-preserving AI into regulated industries and how enterprise adoption reduces data breach risk and regulatory penalties.
Investigates the adoption of privacy-enhancing technologies (PETs) to enable compliant AI and secure data collaboration, including federated learning, encryption, and differential privacy.
Table of Contents
22 Pages
- Executive Summary
- 1. Evolution of Data Privacy Regulation (Post-GDPR Era)
- 2. Market Landscape of Privacy Enhancing Technologies
- 3. Differential Privacy, Federated Learning, and Homomorphic Encryption
- 4. PETs in Finance, Healthcare, and Public Sector
- 5. Investment Outlook and Vendor Analysis
- 6. Integration with AI Governance and Compliance Frameworks
- 7. Strategic Recommendations for Enterprise Adoption
- Appendix – Use Cases and Technical Comparisons
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