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Duration 14 hours
Course Outline
Foundations of Privacy-Preserving AI
- Essential principles of data privacy in mobile application development
- Regulatory factors driving the adoption of on-device AI
- Advantages and constraints associated with local processing
Exploring Nano Banana for On-Device Privacy
- Architectural design of the Nano Banana model
- Security characteristics and local execution mechanisms
- Supported platforms and standard mobile integration patterns
Techniques for Data Management and Local Processing
- Secure collection and storage of sensitive data on the device
- Reducing data exposure through local inference methods
- Strategies for anonymization and pseudonymization
Deploying Privacy-Preserving AI Features
- Building AI-driven functionalities that avoid transmitting user data
- Creating workflows compliant with healthcare, finance, or general regulatory standards
- Guaranteeing data isolation among different application components
Security Implications for On-Device Models
- Safeguarding models against extraction or unauthorized modification
- Implementing secure sandboxing and managing permissions
- Conducting threat modeling for mobile AI architectures
Alignment with Compliance and Regulations
- Navigating the impact of GDPR, HIPAA, and financial sector regulations
- Documenting privacy-by-design methodologies
- Maintaining audit trails without exposing user data
Testing and Verification of Privacy Assurances
- Validating workflows to prevent unintended data leakage
- Analyzing the balance between accuracy and privacy trade-offs
- Performing continuous validation through application updates
Deployment and Lifecycle Management of Privacy-Focused AI Apps
- Overseeing updates for on-device models
- Monitoring performance metrics and compliance status over time
- Preparing applications for evolving regulatory landscapes
Conclusion and Future Directions
Requirements
- A foundational grasp of mobile or application development principles
- Working knowledge of Python, Kotlin, or Swift
- Basic familiarity with core AI or machine learning concepts
Target Audience
- Enterprise development teams
- Compliance and regulatory officers
- Developers responsible for building security-sensitive applications
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