Synthetic & Privacy-Preserving Data Assessment Template

Brief use-case summary (purpose, consumers, high-level workflow)

Describe the analytic goals, who will consume the results, whether models are for internal use, shared with partners, or productized. Mention downstream tasks the data must support (e.g., predictive model training, aggregate reporting, exploratory analysis).

Does the dataset contain personal data or direct identifiers?

Direct identifiers include name, SSN, exact address, phone numbers; personal data may include pseudonymous IDs linked elsewhere.

How sensitive are the attributes (business/health/financial/rare events)?

0 = not sensitive, 10 = extremely sensitive (medical, criminal, financial details, or rare event signals).

1.0 10.0

Does the use case require high-fidelity, row-level realism for model performance?

1 = coarse aggregates only, 5 = requires near-identical row-level patterns and rare event fidelity.

1.0 10.0

Regulatory or contractual constraints that apply

Select any that apply. If Other, note details in Implementation Notes.

GDPR

HIPAA

CCPA

Other (specify below)

Will data or models be shared outside the organization?

Sharing scope affects acceptable risk tolerance and validation requirements.

Privacy risk: identifiability / linkability (0 low — 10 high)

Consider ease of linking records to external sources, uniqueness of combinations, and presence of quasi-identifiers.

1.0 10.0

Privacy risk: impact of re-identification if it occurs (0 low — 10 high)

Consider harm to individuals, legal exposure, and reputational damage.

1.0 10.0

Privacy risk: exploitability (attacker access level, incentives, known external datasets) (0 low — 10 high)

Higher if many external linking datasets exist or adversaries are motivated and capable.

1.0 10.0

Methods under consideration (select all that apply)

Select the approaches you plan to evaluate. You may select multiple for comparison.

Naive anonymization (removal of direct identifiers)

k-anonymity / l-diversity / t-closeness

Differential privacy (DP) — noise/DP mechanisms

Synthetic generation — generative models (GANs, diffusion, language models)

Statistical synthesis / sampling-based methods

Hybrid (synthetic + anonymization or DP)

If Differential Privacy is considered: planned privacy budget and rationale

Record planned epsilon/delta, mechanism (Gaussian/Laplace), accounting method, and acceptance criteria. If DP is not considered, leave blank.

If synthetic generation is considered: model family and training constraints

Note model family (GAN, VAE, diffusion, LLM, task-specific tabular synth), training data partitioning, random seeds, and mechanisms to avoid memorization (e.g., remove outliers, regularization).

Planned utility metrics to validate (select all that apply)

Choose metrics that reflect the real tasks the synthetic data must support.

Distributional similarity (marginals, joint distributions)

Downstream predictive model performance

Feature correlation / covariance preservation

Preservation of rare events / tail behaviour

Aggregate statistics and reporting consistency

Validation dataset and tasks (describe held-out real tests, tasks and success criteria)

Example: hold out 20% of real data; train model on synthetic, test on held-out real; require <= X% degradation in AUC and no substantial shift in key aggregates.

Size of held-out real validation set (rows)

Enter the planned number of rows or fraction of dataset reserved for validation.

Adversarial re-identification testing planned?

Adversarial tests might include record linkage attacks, membership inference, or nearest-neighbor memorization checks.

Yes

Partial / limited tests planned

Implementation caveats & mandatory checks (tick as completed)

These are minimum operational controls to complete before deployment or sharing.

Validate synthetic models against held-out real data

Run adversarial re-id and memorization tests

Stakeholder & legal risk review completed

Provenance recording and versioning controls

Document all generation/privacy parameters and seeds

Regulatory / contractual compliance check

Implementation checklist and caveats (action items, owners, ETA)

List concrete actions, responsible owners, dates, and acceptance criteria. E.g., create synth pipeline, DP config, run validation, legal sign-off.

Monitoring for leakage and drift in production (what to monitor and frequency)

Include metrics, alert thresholds, frequency (daily/weekly/monthly), and response playbook. Consider membership inference detection and data drift signals.

Overall recommendation

Choose the recommendation the team can act on.

Estimated composite privacy risk score (0 low — 100 high)

Optionally calculate a composite score using the privacy risk scales above (example: weighted sum normalized to 100) and enter the result here.

Final notes, open questions, and next steps (owners & timelines)

Record unresolved risks, experiments to run, required approvals, and the next review date.

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Synthetic & Privacy-Preserving Data Assessment Template

Synthetic & Privacy-Preserving Data Assessment

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