AI System Privacy Control Framework

An AI System Privacy Control Framework is a privacy control framework that implements privacy-preserving techniques for AI system data processing and AI model training.

• AKA: AI Privacy Framework, Machine Learning Privacy Control Framework, Privacy-Preserving AI Framework.
• Context:
  • It can typically implement Differential Privacy to add calibrated noise to training data or model outputs.
  • It can typically enable Federated Learning for distributed model training without raw data sharing.
  • It can typically apply Homomorphic Encryption for computation on encrypted data.
  • It can typically enforce Data Minimization Principles through feature selection and data retention limits.
  • It can typically provide Privacy Impact Assessments for AI system deployments.
  • ...
  • It can often utilize Secure Multi-Party Computation for collaborative learning.
  • It can often implement Model Inversion Defenses against privacy attacks.
  • It can often support Consent Management for personal data processing.
  • It can often enable Right to Erasure through machine unlearning techniques.
  • ...
  • It can range from being a Basic AI System Privacy Control Framework to being a Advanced AI System Privacy Control Framework, depending on its privacy technique sophistication.
  • It can range from being a Centralized AI System Privacy Control Framework to being a Decentralized AI System Privacy Control Framework, depending on its architectural approach.
  • ...
  • It can support AI System Data Governance Frameworks through privacy compliance mechanisms.
  • It can integrate with AI System Regulatory Compliance Audit Processes for privacy verification.
  • It can enable Zero-Trust AI System Security Architectures via privacy-aware access control.
  • It can complement Encryption Key Management Systems with privacy-preserving encryption.
  • ...
• Example(s):
  • Healthcare AI System Privacy Control Frameworks, such as:
    • HIPAA-Compliant AI Privacy Framework for medical data processing.
    • Federated Healthcare AI Framework enabling cross-hospital collaboration.
    • Genomic AI Privacy Framework protecting genetic information.
  • Financial AI System Privacy Control Frameworks, such as:
    • PCI-DSS AI Privacy Framework for payment data protection.
    • Open Banking AI Privacy Framework managing financial data sharing.
    • Anti-Money Laundering AI Privacy Framework balancing privacy and compliance.
  • Consumer AI System Privacy Control Frameworks, such as:
    • GDPR-Compliant AI Privacy Framework implementing privacy by design.
    • CCPA AI Privacy Framework enabling consumer privacy rights.
  • ...
• Counter-Example(s):
  • General Privacy Policy, which lacks technical privacy controls for AI systems.
  • Data Security Framework, which focuses on data protection without privacy-specific controls.
  • AI Ethics Framework, which addresses ethical principles rather than technical privacy implementation.
• See: Differential Privacy, Federated Learning, Homomorphic Encryption, AI System Data Governance Framework, Privacy-Preserving Machine Learning, GDPR Compliance Framework, Secure Multi-Party Computation.

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