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Duration 14 hours
Course Outline
Foundations of Privacy-Preserving AI
- Essential principles of data privacy within mobile applications
- Regulatory factors driving the adoption of on-device AI
- Advantages and constraints associated with local data processing
Exploring Nano Banana for On-Device Privacy
- Architectural overview of the Nano Banana model
- Security characteristics and local execution mechanisms
- Compatible platforms and mobile integration approaches
Techniques for Data Handling and Local Processing
- Secure collection and storage of sensitive data on the device
- Reducing data exposure through local inference capabilities
- Strategies for anonymization and pseudonymization
Implementing Privacy-Centric AI Capabilities
- Building AI-driven features that avoid transmitting user data externally
- Designing workflows suitable for healthcare, finance, or strict compliance sectors
- Safeguarding data isolation across different application components
Security Implications for On-Device Models
- Defending models against extraction or unauthorized modification
- Managing secure sandboxing and permission controls
- Threat modeling specific to mobile AI systems
Ensuring Compliance and Regulatory Alignment
- Navigating the implications of GDPR, HIPAA, and financial sector regulations
- Documenting privacy-by-design methodologies
- Preserving auditability while protecting user data integrity
Validating and Testing Privacy Assurances
- Testing workflows to detect potential unintended data leaks
- Balancing model accuracy against privacy requirements
- Performing continuous validation through application updates
Deployment and Long-Term Maintenance of Privacy-First AI Apps
- Overseeing updates to on-device models
- Tracking performance and compliance metrics over time
- Preparing applications for future regulatory changes
Conclusion and Future Directions
Requirements
- Familiarity with mobile or general application development practices
- Working knowledge of Python, Kotlin, or Swift
- Fundamental understanding of AI or machine learning concepts
Target Audience
- Enterprise development teams
- Compliance and governance officers
- Developers creating applications for sensitive or regulated domains
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