Security & Privacy Modeling

PAMOLA incorporates advanced privacy risk modeling techniques, leveraging differential privacy, formal anonymization models, and adversarial attack simulations. These techniques help in identifying vulnerabilities and ensuring compliance with modern privacy standards (e.g., GDPR, CCPA, PIPEDA).

Overview of Security & Privacy Modeling

PAMOLA provides a comprehensive risk modeling framework to evaluate data security and privacy robustness. The key security modeling components include:

  1. Differential Privacy (DP) – A formal mechanism ensuring that queries on a dataset do not reveal individual records.

  2. Privacy Attack Simulations – Evaluating data vulnerability against adversarial attacks.

  3. Formal Anonymization Models – Implementing k-anonymity, l-diversity, t-closeness, and entropy-based inference risk analysis.

  4. Membership & Model Inversion Attacks – Evaluating risks through shadow models and probabilistic reconstruction.

— ### 🔐 Differential Privacy (DP) Differential Privacy provides mathematical guarantees against re-identification risks by injecting controlled noise into query results.

Key Differential Privacy Concepts:

  • Epsilon (ε) Privacy Budget – Controls how much information a query leaks.

  • Laplace & Gaussian Noise Mechanisms – Methods for perturbing numerical and categorical data.

  • Privacy Budget Depletion – Theoretical modeling to measure how long a dataset remains private before reconstruction becomes feasible.

PAMOLA includes mechanisms for enforcing DP, particularly in federated learning, synthetic data generation, and anonymization workflows.

— ### 🛡️ Privacy Attack Modeling PAMOLA includes attack simulation capabilities to evaluate data exposure risks in real-world adversarial settings.

Supported Privacy Attack Models:

  • Single-Out Attack – Identifying unique individuals in an anonymized dataset.

  • Inference Attack – Predicting missing attributes based on entropy-based information gain.

  • Linkage Attack – Combining external datasets to re-identify individuals probabilistically.

  • Membership Inference Attack – Determining whether an individual was part of the training dataset using shadow models.

  • Model Inversion Attack – Extracting sensitive data by reconstructing training data features.

These attack simulations enable data protection officers (DPOs) and compliance teams to test the robustness of privacy measures before deployment.

— ### 🔍 Single-Out Attack: Measuring Individual Identifiability A Single-Out Attack attempts to isolate an individual record from an anonymized dataset.

Formal Modeling:

  • Based on formal k-anonymity constraints.

  • Uses statistical uniqueness tests to measure identifiability risk.

  • Evaluates dataset sparsity and uniqueness across attributes.

Prevention Strategies: - Applying generalization & suppression techniques. - Increasing k-anonymity threshold. - Reducing attribute uniqueness via controlled perturbation.

— ### 📊 Inference Attack: Entropy-Based Risk Modeling An Inference Attack uses information gain metrics to infer missing attributes.

Entropy-Based Modeling:

  • Measures information leakage using Shannon entropy.

  • Computes conditional entropy to determine predictability of missing attributes.

  • Evaluates how correlations between dataset variables contribute to inference risk.

Prevention Strategies: - Introducing l-diversity to ensure each attribute value has sufficient variability. - Applying controlled randomization techniques. - Implementing differential privacy noise mechanisms.

— ### 🔗 Linkage Attack: Probabilistic & Vector-Based Re-Identification A Linkage Attack attempts to re-identify individuals by correlating anonymized records with external data sources.

Two Primary Linkage Models:

  1. Fellegi-Sunter Probabilistic Model – Uses conditional probabilities to match datasets.

  2. Cluster-Vector Probabilistic Linkage (CVPL) – A vector-based matching approach using clustering and similarity scores.

Prevention Strategies: - Implementing t-closeness to maintain distributional similarity across datasets. - Reducing high-risk attributes via generalization and transformation. - Using cryptographic anonymization techniques like Secure Multiparty Computation (SMPC).

— ### 🕵️ Membership Inference Attack (MIA): Shadow Model Simulations A Membership Inference Attack seeks to determine if a given record was included in a model’s training data.

MIA Model:

  • Trains a shadow model that mimics the original model’s behavior.

  • Compares output probabilities between training and non-training samples.

  • Uses differentiated confidence scores to infer dataset membership.

Prevention Strategies: - Applying differential privacy constraints during training. - Limiting model confidence via temperature scaling & adversarial regularization. - Restricting access to model predictions and confidence scores.

— ### 🛠️ Model Inversion Attack: Data Reconstruction from AI Models A Model Inversion Attack attempts to reconstruct training data by exploiting correlations in model outputs.

Model Inversion Techniques:

  • Uses gradient-based optimization to reconstruct training features.

  • Applies adversarial learning to exploit model vulnerabilities.

  • Attacks high-dimensional AI models, particularly image and tabular datasets.

Prevention Strategies: - Limiting model exposure to query-based attacks. - Implementing adversarial training with differential privacy. - Redacting high-sensitivity attributes from model outputs.

— ### 🔒 Privacy Risk Assessment & Security Compliance PAMOLA includes risk scoring models to evaluate the overall privacy posture of datasets and AI systems.

Risk Assessment Components:

  • Dataset Risk Scores – Evaluates dataset re-identifiability and inference risks.

  • AI Model Privacy Scoring – Analyzes membership inference susceptibility.

  • Privacy Budget Calculations – Measures differential privacy budget depletion over time.

— ### ** Next Steps** - To understand how to configure privacy models and simulations, visit the [System Guide](system_guide.html). - For developer integration of privacy-enhancing transformations, refer to the [Developer Guide](developer_guide.html). - Explore real-world applications of PAMOLA’s security modeling in the [Use Cases](use_cases.html) section.

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