Privacy-enhancing technologies can help teams build or share AI models using sensitive or distributed data, but none guarantees that private information cannot be exposed. Federated learning can keep raw records from being pooled in one central dataset; differential privacy can quantify and limit privacy loss; and other methods can protect joint computation or data releases. The right choice depends on what must be protected, from whom, and at which stage of model development.
What does privacy tech protect during AI model building?
Privacy risks can arise when a team collects data, runs training, exchanges information between collaborators, or releases a model or dataset. A useful starting point is to identify the sensitive information, who should not see it, where computation will happen, and what might be exposed: raw inputs, intermediate updates, model outputs, or generated data.
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These distinctions matter because a method that limits access to source records may not prevent someone from learning information from a trained model. NIST’s federated learning explainer notes that models can leak training data, including images and text snippets. Privacy controls therefore need to be assessed across the full workflow, not only at the point where data is stored.
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The methods below address different parts of the problem. The OECD’s June 2025 paper, Sharing trustworthy AI models with privacy-enhancing technologies, describes PETs for confidential or minimal input use as well as confidential model co-creation and sharing. It also cautions that combining methods can offset some limitations, while balancing utility, efficiency, and usability remains difficult.
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| Approach | What it contributes | What still needs scrutiny |
|---|---|---|
| Federated learning | Participants train across distributed data without pooling raw training records in one central dataset. | Updates and resulting models can still reveal sensitive information. Assess what observers can infer, how participants and aggregation are protected, and what the model may disclose. NIST, “Federated Learning.” |
| Differential privacy | A mathematical framework for quantifying privacy loss; adding noise can limit what outputs reveal about individual training examples. | Review the documented guarantee, how privacy loss is accounted for, implementation assumptions, and the impact on model utility. NIST SP 800-226 (2025). |
| Secure multi-party computation | Allows parties to compute jointly while limiting disclosure of their private inputs. | Check the protocol’s threat model and the efficiency and usability costs for the intended workload. OECD (2025). |
| Homomorphic encryption | Supports computation on encrypted data for confidential collaboration. | Determine whether the operations and performance constraints suit the workload. OECD (2025). |
| Trusted execution environments | Provide a protected execution setting for sensitive computation. | Identify the trust assumptions, deployment controls, and residual risks for the specific environment. OECD (2025). |
| Synthetic data | Can support exploratory development and testing without giving researchers direct access to source records. | Test whether the generated data discloses records or attributes, and whether it preserves properties needed for the task. OECD (2025). |
Federated learning: keep records distributed, then examine updates
Federated learning is principally a data-location strategy: participants train using data held at their locations and exchange updates rather than collecting all raw training records in one central dataset. This can reduce exposure caused by centralizing records, but it does not establish that updates or the resulting model are private. NIST documents that trained models can leak training data, so teams should consider both what leaves each participant and what can be inferred from the final model.
Differential privacy: demand the actual guarantee
Differential privacy provides a mathematical way to quantify privacy loss. In practical systems, the guarantee depends on the implementation and how privacy loss is tracked; the label alone is not enough to assess protection. NIST’s Guidelines for Evaluating Differential Privacy Guarantees (SP 800-226), finalized March 6, 2025, describe the framework and warn about hazards in real software. The NIST page was updated August 29, 2025.
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Privacy and model utility must be evaluated together. NIST notes that adding more noise generally improves privacy but can reduce accuracy, and that large neural networks can be harder to train with differential privacy than simpler model classes. There is no single utility result that applies to every dataset and task; evaluate the proposed configuration on the intended use case.
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Secure multi-party computation and homomorphic encryption can support collaboration without simply handing every party the same private inputs in readable form. Trusted execution environments offer a protected setting in which sensitive computation can run. These approaches make different assumptions and impose different workload constraints; the OECD’s 2025 paper identifies them as relevant AI PETs but does not establish a universal performance or security result for a particular implementation.
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Synthetic data: useful does not mean anonymous
Synthetic data may let teams explore or test without directly exposing source records, but generated examples should not be presumed anonymous. The OECD discusses model inversion, membership inference, and attribute disclosure risks. It also describes differential privacy as an additional safeguard in some use cases. Assess generated data against the disclosure risks relevant to its intended recipients and use.
How should a team choose and validate an approach?
- Define the threat model. List the sensitive data, the people or organizations that must not see it, where computation occurs, and whether the main concern is access to inputs, updates, model outputs, or synthetic releases.
- Map the workflow. Identify where records are stored, what information crosses organizational boundaries, who can inspect intermediate results, and what will be shared at the end.
- Match controls to the exposure. If the concern is central collection, consider a distributed-training design; if the concern is inference from outputs, evaluate an output-protection approach such as differential privacy. For joint computation or encrypted collaboration, assess the relevant secure computation or encryption method. These are starting points, not guarantees.
- Document assumptions and residual risk. For each method, record which parties are trusted, what information remains visible, and what attacks or disclosure routes are not addressed. A control should not be described as protecting a stage it does not cover.
- Measure privacy and utility in the intended setting. Record the privacy guarantee and accounting method where applicable, then evaluate the resulting model or data for the task it must serve. Include operational feasibility: compute, communication, usability, and the complexity of maintaining the control.
- Review governance for the relevant jurisdictions. Check applicable privacy and AI obligations for the locations and participants involved. A PET by itself does not establish compliance with a particular law.
What questions should reviewers ask?
- Which raw inputs, updates, intermediate computations, outputs, or generated records can each participant see?
- What is the stated threat model, and which parties or infrastructure components must be trusted?
- For differential privacy, what guarantee is documented, how is privacy loss accounted for, and what implementation assumptions apply?
- How was utility evaluated for the intended model and task, and what changed when privacy controls were enabled?
- What compute, communication, usability, and operational burdens does the approach add?
- For synthetic data, what tests address membership inference, model inversion, or attribute disclosure?
- Which jurisdiction-specific legal and governance requirements apply to the data, participants, and model release?
Why does jurisdiction matter?
Privacy and AI governance are not organized identically across jurisdictions. The OECD’s June 2024 paper, AI, data governance and privacy: Synergies and areas of international co-operation, describes how these issues are often handled in separate policy silos and how that can complicate compliance and cooperation. Teams working across borders should assess the rules relevant to their specific participants and data flows rather than treating a PET as a substitute for legal review.
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