What this covers
PETs change what is possible: analysis without disclosure, model training without data movement, verification without revelation. They also carry real engineering cost and are frequently oversold. The value of this engagement is as much in ruling technologies out as in deploying them.
- Use-case assessment — which problem a PET would actually solve here
- Differential privacy for aggregate release and telemetry
- Federated learning where data cannot move but models can
- Synthetic data and de-identification for development and analytics
- Homomorphic encryption, zero-knowledge proofs and secure multi-party computation, scoped realistically
- Regulatory analysis — what the deployment changes about your lawful basis and transfer position, and what it does not
An important caveat
A PET reduces risk; it rarely removes an obligation entirely. Regulators assess residual re-identification risk, not the elegance of the technique. Any deployment here comes with a written analysis of what the regime still requires.
What you get
A short list of technologies worth the investment for your specific problem, a pilot design, and the regulatory argument that goes with it.