Ai model privacy audit

AI Model Privacy Audit

Overview

AI model privacy auditing is the systematic assessment of whether trained ML models leak information about their training data. Models can memorize individual training records, enabling adversaries to extract personal data, determine dataset membership, reconstruct input features, or infer sensitive attributes. This skill implements a comprehensive model privacy audit methodology using established attack techniques and tools (ML Privacy Meter, ART, Foolbox) to quantify privacy leakage before deployment and periodically during operation. The audit results feed directly into the AI DPIA risk assessment and inform mitigation measure selection.

Privacy Attack Taxonomy

1. Training Data Extraction

Objective: Extract verbatim or near-verbatim records from the model's training data.

Attack Vector	Description	Target Models
Prompt-based extraction	Craft prompts that cause LLMs to regurgitate training data	Language models, generative models
Canary extraction	Insert known canary strings into training data and test if model reproduces them	Any model (testing methodology)
Gradient-based extraction	Use model gradients to reconstruct training inputs	Models with accessible gradients
Generative reconstruction	Use the model as an oracle to iteratively reconstruct training samples	GANs, VAEs, diffusion models

Risk Factors Increasing Extraction Likelihood:

• Large model capacity relative to training data size (overfitting)
• Training data containing duplicated or near-duplicated records
• Longer training duration (more epochs)
• Lower regularisation
• Models with high output granularity (logits, probabilities)

Testing Methodology:

1. Insert canary records with unique identifiers into training data
2. Train the model
3. Attempt extraction through various prompting strategies
4. Measure extraction success rate (percentage of canaries recovered)
5. Threshold: extraction rate should be below 0.1% for acceptable risk

2. Membership Inference

Objective: Determine whether a specific record was in the model's training set.

Attack Type	Method	Computational Cost
Shadow model attack	Train shadow models on similar data, build a binary classifier on model outputs	High — requires training multiple shadow models
Metric-based attack	Use model confidence, loss, or entropy to distinguish members from non-members	Low — single model query per sample
Label-only attack	Use predicted labels (no confidence scores) to infer membership	Medium — requires multiple queries
Likelihood ratio attack (LiRA)	Compare per-sample loss to reference distributions	High — most accurate, requires multiple models

ML Privacy Meter Implementation:

• Population metric-based attack: compares target model's loss on a sample against population loss distribution
• Reference metric-based attack: uses reference models to compute per-sample metrics
• Shadow model attack: trains shadow models and uses the attack model to classify members

Testing Methodology:

1. Partition data: training set (members) and held-out set (non-members)
2. Run membership inference attacks using ML Privacy Meter
3. Measure attack success: true positive rate at low false positive rate (TPR@FPR=0.1%, 1%)
4. Generate ROC curves per sample and aggregate
5. Threshold: TPR@1%FPR should be below 5% for acceptable privacy

3. Model Inversion

Objective: Reconstruct input features from model outputs.

Attack Type	Method	Target
Confidence-based inversion	Iteratively optimise input to maximise model confidence for a known label	Classification models
Gradient-based inversion	Use model gradients to reconstruct inputs from outputs	White-box models
GAN-based inversion	Train a GAN to invert model outputs to input space	Face recognition, image classifiers

Testing Methodology:

1. Select target classes or individuals
2. Run inversion attacks with various initializations
3. Measure reconstruction quality (SSIM, PSNR for images; cosine similarity for embeddings)
4. Assess re-identification risk: can reconstructed data identify specific individuals?
5. Threshold: reconstruction similarity should be below 0.3 (SSIM) for acceptable risk

4. Attribute Inference

Objective: Infer sensitive attributes not present in the model's output.

Attack Type	Description
Correlation exploitation	Use correlated features to infer sensitive attributes from model behaviour
Partial knowledge attack	Attacker knows some attributes and uses model to infer remaining sensitive ones
Group inference	Determine statistical properties of training subgroups

Testing Methodology:

1. Identify sensitive attributes (Art. 9 categories) that may be correlated with model features
2. Train attack models to predict sensitive attributes from model outputs
3. Measure inference accuracy for each sensitive attribute
4. Compare against random baseline
5. Threshold: inference accuracy should not exceed random baseline + 10%

Audit Methodology

Phase 1: Audit Scoping (Days 1-3)

1. Define audit scope: which models, what deployment context, what threat model
2. Identify assets: training data, model artefacts, deployment infrastructure
3. Define threat model: who are the adversaries, what access do they have?
   • Black-box: API access only (queries and responses)
   • Grey-box: API access plus model architecture knowledge
   • White-box: Full access to model weights and architecture
4. Select attacks based on threat model and model type
5. Define success criteria (acceptable leakage thresholds)
6. Obtain audit authorisation from model owner and legal

Phase 2: Environment Setup (Days 4-7)

1. Set up isolated audit environment (no production data leakage)
2. Install audit tools: ML Privacy Meter, ART (Adversarial Robustness Toolbox), custom scripts
3. Obtain model access (API endpoint or model weights depending on threat model)
4. Prepare member/non-member datasets for membership inference
5. Prepare canary data for extraction testing
6. Configure monitoring to log all audit queries

Phase 3: Attack Execution (Days 8-18)

For each selected attack:

1. Configure attack parameters
2. Execute attack against the target model
3. Collect results (success rates, confidence intervals)
4. Vary attack parameters to find worst-case leakage
5. Document: attack configuration, results, computational cost

Phase 4: Analysis and Reporting (Days 19-25)

1. Aggregate results across all attacks
2. Calculate privacy risk scores per attack type
3. Identify high-risk data subsets (records most vulnerable to extraction)
4. Cross-reference with DPIA risk register
5. Generate audit report with:
   • Executive summary
   • Attack results per category
   • Risk assessment with GDPR alignment
   • Recommended mitigations
   • Residual risk after proposed mitigations

Phase 5: Remediation Validation (Days 26-30)

1. If mitigations are applied (differential privacy, output perturbation, etc.)
2. Re-run key attacks to validate mitigation effectiveness
3. Document residual leakage post-mitigation
4. Compare against acceptable thresholds
5. Issue final audit certificate or remediation requirements

Privacy Leakage Thresholds

Metric	Acceptable	Elevated	Unacceptable
Membership inference TPR@1%FPR	< 5%	5-15%	> 15%
Training data extraction rate	< 0.1%	0.1-1%	> 1%
Model inversion SSIM	< 0.3	0.3-0.6	> 0.6
Attribute inference accuracy above baseline	< 10%	10-25%	> 25%

Mitigation Measures

Mitigation	Attacks Mitigated	Trade-off
Differential privacy (DP-SGD)	All — provides mathematical guarantee	Model accuracy reduction (calibrate epsilon)
Training data deduplication	Extraction, membership inference	One-time preprocessing cost
Regularisation (dropout, weight decay)	Membership inference, overfitting-related leakage	May affect model performance
Output perturbation	Model inversion, attribute inference	Reduces output precision
Confidence score rounding	Metric-based membership inference	Minor output precision loss
Model distillation	Extraction, membership inference	Requires additional training
Rate limiting	All query-based attacks	Affects legitimate use
Input/output PII filtering	Extraction of PII from generative models	May affect model utility

Tools and Frameworks

Tool	Purpose	Source
ML Privacy Meter	Membership inference auditing	github.com/privacytrustlab/ml_privacy_meter
IBM ART	Adversarial robustness and privacy testing	github.com/Trusted-AI/adversarial-robustness-toolbox
TensorFlow Privacy	Differential privacy training	github.com/tensorflow/privacy
Opacus	PyTorch differential privacy	github.com/pytorch/opacus
Google DP Library	Differential privacy algorithms	github.com/google/differential-privacy
Foolbox	Adversarial attack library	github.com/bethgelab/foolbox

Enforcement Relevance

Model privacy auditing is not explicitly required by the GDPR or AI Act, but is effectively mandated through:

• Art. 35 DPIA: Risk assessment for AI systems must evaluate privacy leakage risks — auditing is the standard methodology
• Art. 32 Security: Appropriate technical measures to ensure security of processing — privacy auditing validates these measures
• AI Act Art. 9: Risk management for high-risk AI requires identification and mitigation of privacy risks
• AI Act Art. 15: Accuracy, robustness, and cybersecurity requirements — privacy attacks are a cybersecurity concern
• EDPB Guidelines 04/2025: Controllers must assess whether AI models have effectively anonymised training data — auditing tests this claim

Integration Points

• ai-dpia: Audit results feed into DPIA Phase 3 risk assessment
• ai-data-retention: Audit validates whether deletion from training data is effective
• ai-deployment-checklist: Pre-deployment privacy audit is a checklist requirement
• ai-federated-learning: Federated learning models require distributed privacy auditing