Privacy-Enhancing Technologies (PETs)
Techniques that reduce privacy risk while enabling data analysis.
How the laws define this
Both frameworks agree that Privacy-Enhancing Technologies preserve privacy and minimize privacy risk, specifically sharing differential privacy, federated learning, and homomorphic encryption as key examples. Their primary divergence lies in their scope, methods, and target applications. NPC Advisory No. 2024-04 confines these technologies to technical measures and techniques designed to maximize data security and minimize personal data use or disclosure during AI development and operation, explicitly adding synthetic data, anonymization, and pseudonymization to its listed examples. Conversely, the National Innovation Centre for Privacy Enhancing Technologies (NICPET) includes organisational measures alongside technical ones, while highlighting secure multi-party computation. Furthermore, NICPET targets broader applications by emphasizing useful data analysis, collaboration, and privacy-preserving public-sector data use, whereas NPC Advisory No. 2024-04 focuses specifically on AI systems processing personal data.
Synthesised from the 2 statutory definitions below. Each one is quoted in full, with its source.
How jurisdictions differ
What is distinctive in each definition, binding law in force first.
| Jurisdiction | Instrument | Status | What is distinctive |
|---|---|---|---|
| Philippines | NPC Advisory No. 2024-04: Guidelines on the Application of the Data Privacy Act to AI Systems Processing Personal Data | In Force | This definition ties PETs specifically to AI development and operation phases, listing examples like differential privacy, federated learning, homomorphic encryption, synthetic data, anonymization, and pseudonymization. |
| Netherlands | National Innovation Centre for Privacy Enhancing Technologies (NICPET) | In Force | This definition includes both technical and organisational measures, contextualizes PETs for public-sector data use, and emphasizes evaluation, piloting, and operationalization. |
Definitions (2)
Technical and organisational measures (e.g., differential privacy, secure multi-party computation, federated learning, homomorphic encryption) designed to minimise privacy risk while enabling useful data analysis and collaboration. In the NICPET context PETs are the technical approaches evaluated, piloted and operationalised to enable privacy-preserving public-sector data use.
Technical measures and techniques that minimize the use or disclosure of personal data, maximize data security, and help preserve data subject privacy during AI development and operation; examples include differential privacy, federated learning, homomorphic encryption, synthetic data, anonymization, and pseudonymization.
Not to be confused with
The related term uses a singular 'Technology' while these definitions use plural 'Technologies' encompassing multiple techniques.
Privacy-by-design focuses on incorporating data protection into system design, whereas PETs are specific technical and organisational measures to minimize privacy risks.
Questions and answers
What examples of PETs are given in the Philippines NPC Advisory?
Examples include differential privacy, federated learning, homomorphic encryption, synthetic data, anonymization, and pseudonymization. These minimize personal data use or disclosure during AI processing.
How does the Netherlands NICPET apply PETs?
NICPET evaluates, pilots, and operationalizes PETs like differential privacy, secure multi-party computation, federated learning, and homomorphic encryption for privacy-preserving public-sector data collaboration.
Do PET definitions cover organisational measures?
The Netherlands NICPET definition explicitly includes both technical and organisational measures to minimize privacy risk.
Related Terms
Privacy Enhancing Technology (PET)
Techniques to reduce privacy risks in data processing....
Data Partnership
Formal collaboration for joint dataset analysis or sharing....
Pilot
Time-limited experiment implementing PETs....
Privacy-by-design
Incorporating data protection into systems' design and architecture....
NAIRR Secure
Pilot track for privacy-preserving, controlled-access research....