Privacy-Preserving AI Systems

Exercise 1: Build a Differential Privacy System#

Implement a production-ready differential privacy system:

1. Create a privacy budget management system with composition tracking
2. Implement multiple noise mechanisms (Laplace, Gaussian, Exponential)
3. Build a query interface with automatic sensitivity calculation
4. Develop privacy accounting with RDP composition
5. Create comprehensive privacy auditing and testing framework

Exercise 2: Federated Learning with Homomorphic Encryption#

Build a secure federated learning system:

1. Implement client-server federated learning architecture
2. Add homomorphic encryption for secure aggregation
3. Create intelligent client selection algorithms
4. Build communication compression and optimization
5. Develop privacy-preserving model evaluation metrics

Exercise 3: Enterprise Privacy Governance Platform#

Create a comprehensive privacy governance system:

1. Design granular consent management with user interfaces
2. Implement automated privacy impact assessments
3. Build regulatory compliance monitoring (GDPR, CCPA, HIPAA)
4. Create privacy-preserving data processing pipelines
5. Develop incident response and breach notification systems

Exercise 4: Privacy Attack Simulation and Defense#

Build advanced privacy testing framework:

1. Implement membership inference attack simulations
2. Create attribute inference testing capabilities
3. Build reconstruction attack evaluation
4. Develop privacy leakage detection algorithms
5. Create automated defense recommendation systems