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Privacy-enhancing technologies (PETs) can help teams train, evaluate, or share AI models using sensitive or distributed data while limiting exposure. They do not provide one universal privacy guarantee: federated learning can keep raw records distributed, for example, but model updates and the trained model may still reveal information. The right safeguards depend on what must remain private, from whom, and at which stage of the model’s lifecycle.
What privacy problem are you trying to solve?
Start by identifying the sensitive information, the parties who must not see it, and where computation will take place. Then pinpoint the exposure you need to control: access to input records, information in training updates, inferences from a trained model, or disclosures from synthetic data.
- Raw-input confidentiality: Can a central service or another participant inspect the underlying records?
- Update confidentiality: Can someone infer information from the values exchanged during training?
- Output confidentiality: Can a user extract sensitive training details from model responses, parameters, or released datasets?
- Operational and legal constraints: Which parties must be trusted, what deployment controls are practical, and which jurisdictions’ rules apply?
These questions are related but not interchangeable. A design that limits access to source records may still need safeguards against information leaking through updates or model outputs.
How do the main privacy techniques differ?
Each approach protects a different part of the workflow and introduces distinct trust, performance, and implementation questions. The OECD’s June 2025 report, Sharing trustworthy AI models with privacy-enhancing technologies, groups these approaches across confidential input use and confidential model co-creation or sharing; it cautions that combining techniques can address limitations, while balancing utility, efficiency, and usability remains difficult.
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| Approach | What it contributes | What to examine |
|---|---|---|
| Federated learning | Participants train across distributed data without pooling raw training records in one central dataset. | Updates and the resulting model can still reveal sensitive information. Check what an observer can infer, how participants and aggregation are protected, and how model outputs are controlled. |
| Differential privacy | A mathematical framework for quantifying privacy loss. Adding noise can limit what outputs reveal about individual training examples. | Establish the actual guarantee, accounting method, implementation assumptions, and utility impact. A label alone does not show how privacy loss is measured or controlled. |
| Secure multi-party computation | Allows parties to compute jointly while limiting disclosure of their private inputs. | Identify the protocol and threat model, then assess its efficiency and usability for the intended workload. |
| Homomorphic encryption | Supports computation on encrypted data for confidential collaboration. | Determine whether the supported operations and performance constraints fit the workload. |
| Trusted execution environments | Provide a protected execution setting for sensitive computation. | Document trust assumptions, deployment controls, and residual risks. The technique’s name by itself does not establish the security of a particular implementation. |
| Synthetic data | Can support exploratory development and testing without giving researchers direct access to source records. | Test whether generated data preserves useful properties without disclosing records or attributes. Synthetic data is not automatically anonymous. |
Does federated learning protect privacy?
It can reduce one exposure: the need to gather participants’ raw training records into a single central dataset. That is a data-location strategy, not a complete privacy guarantee. Training updates and the final model can still reveal information about their training data. NIST’s federated-learning explainer notes that trained models may leak training data, including images and text snippets.
For a federated design, assess who can see each update, how updates are combined, what participants are trusted to do, and what information can be inferred from the released model. Keeping records local does not, by itself, answer those questions.
What does differential privacy guarantee—and what should you verify?
Differential privacy provides a mathematical framework for quantifying privacy loss. In model building, noise can limit how much an output reveals about individual training examples. The privacy guarantee depends on the implemented mechanism and its accounting, rather than on the term “differential privacy” appearing in a product or project description.
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- The documented guarantee and the mechanism that provides it.
- The method used to account for privacy loss across training and releases.
- The assumptions and implementation details on which the guarantee depends.
- An evaluation of model utility under the chosen privacy settings.
Privacy and utility must be measured together: more noise generally improves privacy but can reduce accuracy. NIST also notes that large neural networks can be harder to train with differential privacy than simpler model classes, so results from one model or task should not be assumed to transfer to another.
When is synthetic data safe to use?
Treat synthetic data as potentially sensitive until it has been evaluated for the intended release and threat. Generated records may preserve or expose details about the source data. The OECD report identifies model inversion, membership inference, and attribute disclosure as risks; differential privacy may provide an additional safeguard in some use cases.
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Assess both sides of the decision: whether the synthetic data remains useful for its intended development or testing task, and whether it reveals records or attributes that should remain private. A dataset being generated rather than directly collected does not establish that it is anonymous.
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Compare candidate designs against the same threat model rather than treating any technique as a universal winner. A useful review covers:
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- Input confidentiality: Which parties can access raw data during collection, training, and evaluation?
- Inference risk: What could an observer learn from updates, model outputs, or synthetic releases?
- Trust assumptions: Which participants, operators, protocols, or execution environments must be trusted?
- Privacy and utility: What privacy guarantee is achieved, and how does it affect the model’s intended use?
- Efficiency and operations: What computation, communication, usability, and deployment complexity does the design add?
- Governance: Which laws and organizational requirements apply to the data, model, participants, and release?
Combining PETs may compensate for individual limitations, but it also adds choices and operational burden. Select controls according to the exposures that matter in the deployment, and validate the complete system—including its updates and outputs—rather than relying on a technique’s name as proof of privacy.
Do PETs satisfy privacy laws?
Adopting a PET alone does not establish compliance with a particular law. Requirements and AI governance differ across jurisdictions, and privacy and AI policy are often handled in separate governance silos. The OECD’s June 2024 paper, AI, data governance and privacy: Synergies and areas of international co-operation, describes these coordination challenges. Determine the applicable rules for the specific data and deployment, and evaluate the technical safeguards as one part of that governance work.
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