Privacy Enhancing Technologies

[All Actors]
1. Conduct Risk and Privacy Impact Analysis
a. Define Privacy risk and impact assessment framework which encompasses AI specific privacy risks.
b. Document the purpose, scope and intended outcome of AI system.
c. Implement a risk classification system (Low, moderate, high) based on use case and data sensitivity.
d. Update the Privacy risk and impact assessment framework as new risks are identified, along with regulation changes.

2. Selection of Privacy Enhancing Technologies (PETs) aligned with Business Objectives
a. Establish guidelines to facilitate selection of PET based on risk profile and business objective, such as;
  a.Low Risk - Data minimization, pseudonymization, and anonymization.
  b.Moderate Risk - Differential Privacy, synthetic data,secure data enclaves.
  c.High Risk - Federated learning, secure multi-party computation (SMPC) or homomorphic encryption.
b. Document selected PETs, and engage legal, compliance and audit team stakeholders for review.
c. Conduct periodic evaluation as business use case evolves.

3. List of Privacy Enhancing Technologies for Training Data - Organization should consider selecting PETs aligned with their business use case and results of privacy impact assessment to mitigate identified risks
  a. Differential Privacy- Mathematical noise added to data or model outputs to prevent leakage of individual data points.
  b. Business Use Case- Training sensitive datasets without revealing specific individual records.
  c. Federated Learning- AI models are trained locally on servers and model updates are aggregated.
  d. Business Use case- Limitation on sharing raw data due to privacy or proprietary concerns.C.Homomorphic Encryption- AI models are trained with encrypted data computations.
  e.Business Use Case- Limitation of sharing unencrypted data due to privacy concerns.
  f. Confidential Computing - AI models are trained in hardware-based Trusted Execution Environment (TEEs), known as secure enclaves.
  g. Business Use Case - Data to be processed in secure hardware environments such as financial transactions.Secure Multi-Party Computation (SMPC) - AI models are trained on split data obtained from multiple parties.
  h. Business Use Case - Multiple parties need to collaborate on private data without revealing individual inputs.
  i. Data Anonymization - AI models are trained on datasets which have removed or masked identifiable information.
  j. Business Use Case - Data sharing should consider privacy law or regulatory standards.
  k..Data Pseudonymization - AI models are trained on datasets which have replaced identifiable information with reversible tokens or identifiers.
  l. Business Use Case - Data should remain linkable for analysis and business operations.

4. Monitoring and Review
a. Monitor privacy metrics, and perform periodic compliance assessments to verify Privacy Enhancing Technologies (PETs) effectively meet privacy objectives, regulatory requirements, and business use case specifications.
b. Evaluate and monitor privacy enhancing technologies effectiveness.
c. Establish performance baselines and acceptable latency thresholds for each PET implementation.

1. Verify that infrastructure-level PETs (e.g., encrypted computation environments, secure aggregation) are implemented based on clearly defined business use cases.

2. Verify that PET infrastructure components are continuously monitored and evaluated to detect degradation or new risks.

3. Verify that PETs offered as services (e.g., secure multiparty computation platforms) meet privacy compliance standards.

4. Verify that metrics and audit reports for infrastructure-level PETs are defined and monitored for effectiveness.

5. Verify that DevOps and infrastructure teams are trained on PET deployment, updates, and security maintenance.

6. Verify that PET systems hosted in the infrastructure are kept up to date with relevant security patches.

7. Verify that PET usage logs (e.g., access to secure enclaves, computation results) are reviewed and analyzed.

8. Verify that periodic third-party penetration tests and vulnerability assessments are conducted on PET-enabled infrastructure offerings.