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Duration 14 hours
Course Outline
Foundations of Privacy-Preserving AI
- Essential data privacy principles in mobile applications
- Regulatory factors driving the adoption of on-device AI
- Advantages and constraints of local data processing
Nano Banana for On-Device Privacy
- Architectural design of the Nano Banana model
- Security features and local execution mechanisms
- Compatible platforms and mobile integration approaches
Data Management and Local Processing Methods
- Secure collection and storage of sensitive data on the device
- Reducing data exposure through local inference
- Strategies for anonymization and pseudonymization
Building Privacy-Centric AI Features
- Creating AI functionalities without transmitting user data externally
- Designing workflows compliant with healthcare, financial, and regulatory standards
- Ensuring data isolation between different app components
Security Aspects of On-Device Models
- Safeguarding models against extraction or tampering
- Implementing secure sandboxing and permission controls
- Conducting threat modeling for mobile AI systems
Compliance and Regulatory Adherence
- Implications of GDPR, HIPAA, and financial sector regulations
- Documenting privacy-by-design methodologies
- Preserving auditability while protecting user data integrity
Verifying Privacy Guarantees Through Testing
- Testing workflows to prevent inadvertent data leaks
- Balancing accuracy against privacy requirements
- Ongoing validation throughout application updates
Deploying and Maintaining Privacy-Focused AI Apps
- Handling updates for on-device models
- Monitoring performance and compliance over time
- Preparing applications for future regulatory changes
Conclusion and Future Directions
Requirements
- Foundational knowledge of mobile or application development
- Proficiency in Python, Kotlin, or Swift
- Basic understanding of AI or machine learning principles
Target Audience
- Enterprise technology teams
- Compliance professionals
- Developers creating applications handling sensitive data
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