Privacy Enhancing Technologies (PETs)
The umbrella term for technologies enabling data utilization while maintaining privacy: DP, FHE, SMPC, TEEs, synthetic data, and more.
PETs is the umbrella term for all privacy-enhancing technologies – from anonymization to homomorphic encryption. Enabler for GDPR-compliant AI.
Explanation
PETs form a spectrum from simple (anonymization, pseudonymization) to complex (FHE, SMPC). The choice depends on threat model, performance requirements, and regulatory context.
Marketing Relevance
Gartner predicts 60% of large enterprises will use PETs for analytics by 2025. GDPR and AI Act make PETs a business enabler.
Example
A pharma company uses a PETs stack: synthetic data for development, DP for analytics, clean rooms for cross-company studies.
Common Pitfalls
Treating PETs as a silver bullet without threat modeling. Different PETs for different use cases – no one-size-fits-all.
Origin & History
The term PETs was coined in the 1990s. The EU data protection group recommended PETs in 2007. Gartner has listed PETs as a top trend since 2020. The EU AI Act (2024) and Data Act accelerate adoption.
Comparisons & Differences
Privacy Enhancing Technologies (PETs) vs. Privacy-Preserving ML
PPML specifically focuses on ML workflows; PETs is the broader umbrella term for all privacy-enhancing technologies.