Privacy
22 techniques
Protecting personal data and maintaining confidentiality in AI systems.
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All techniques
22 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +6 | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| GAN-Based Tabular Synthetic Data | Algorithmic | Architecture/neural Networks/generative/gan Architecture/neural Networks/generative/vae +4 | Tabular | Generates synthetic tabular datasets using Generative Adversarial Networks, most commonly through architectures such as... | ||
| Simulation-Based Synthetic Data Generation | Algorithmic | Requirements/model Agnostic Paradigm/generative +1 | Tabular Time-series | Generates synthetic datasets through computational simulation of underlying data-generating processes, encompassing... | ||
| Federated Learning | Algorithmic | Architecture/linear Models Architecture/neural Networks +4 | Any | Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or... | ||
| Differential Privacy | Algorithmic | Architecture/model Agnostic Requirements/black Box +1 | Any | Differential privacy provides mathematically rigorous privacy protection by adding carefully calibrated random noise to... | ||
| Homomorphic Encryption | Algorithmic | Architecture/linear Models Architecture/model Agnostic +4 | Any | Homomorphic encryption allows computation on encrypted data without decrypting it first, producing encrypted results... | ||
| Cross-validation | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Cross-validation evaluates model performance and robustness by systematically partitioning data into multiple subsets... | ||
| Internal Review Boards | Process | Architecture/model Agnostic Requirements/black Box | Any | Internal Review Boards (IRBs) provide independent, systematic evaluation of AI/ML projects throughout their lifecycle to... | ||
| Red Teaming | Procedural | Architecture/model Agnostic Requirements/black Box | Any | Red teaming is a structured adversarial evaluation process in which a dedicated team systematically probes an AI/ML... | ||
| Human-in-the-Loop Safeguards | Process | Architecture/model Agnostic Requirements/black Box | Any | Human-in-the-loop safeguards establish systematic checkpoints where human experts review, validate, or override AI/ML... | ||
| Confidence Thresholding | Algorithmic | Architecture/model Agnostic Requirements/black Box +1 | Any | Confidence thresholding creates decision boundaries based on model uncertainty scores, routing predictions into... | ||
| Runtime Monitoring and Circuit Breakers | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | Runtime monitoring and circuit breakers establish continuous surveillance of AI/ML systems in production, tracking... | ||
| Model Cards | Documentation | Architecture/model Agnostic Requirements/black Box | Any | Model cards are standardised documentation frameworks that systematically document machine learning models through... | ||
| Datasheets for Datasets | Documentation | Architecture/model Agnostic Requirements/black Box | Any | Datasheets for datasets establish comprehensive documentation standards for datasets, systematically recording creation... | ||
| MLflow Experiment Tracking | Process | Architecture/model Agnostic Requirements/black Box | Any | MLflow is an open-source platform that tracks machine learning experiments by automatically logging parameters, metrics,... | ||
| Data Version Control | Process | Architecture/model Agnostic Requirements/black Box | Any | Data Version Control (DVC) is a Git-like version control system specifically designed for machine learning data, models,... | ||
| Fairness GAN | Algorithmic | Architecture/neural Networks/generative/gan Paradigm/generative +4 | Any | A data generation technique that employs Generative Adversarial Networks (GANs) to create fair synthetic datasets by... | ||
| Model Development Audit Trails | Procedural | Architecture/model Agnostic Requirements/black Box | Any | Model development audit trails create comprehensive, immutable records of all decisions, experiments, and changes... | ||
| Machine Unlearning | Algorithmic | Architecture/model Agnostic Architecture/neural Networks +2 | Any | Machine unlearning enables removal of specific training data's influence from trained models without complete... | ||
| Model Extraction Defence Testing | Testing | Architecture/model Agnostic Requirements/black Box | Any | Model extraction defence testing evaluates protections against attackers who attempt to steal model functionality by... |
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