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Course Outline
Introduction to Privacy-Preserving AI
- Fundamental principles of data privacy in mobile applications
- Regulatory motivations for on-device AI
- Advantages and constraints of local processing
Understanding Nano Banana for On-Device Privacy
- Architecture of Nano Banana models
- Security attributes and local execution pathways
- Supported platforms and mobile integration patterns
Data Handling and Local Processing Techniques
- Secure collection and storage of sensitive data on the device
- Reducing data exposure through local inference
- Strategies for anonymization and pseudonymization
Implementing Privacy-Preserving AI Features
- Developing AI-driven features without transmitting user data
- Designing workflows suitable for healthcare, finance, or compliance requirements
- Ensuring data isolation across application components
Security Considerations for On-Device Models
- Protecting models from extraction or tampering
- Secure sandboxing and permission management
- Threat modeling for mobile AI systems
Compliance and Regulatory Alignment
- Understanding implications of GDPR, HIPAA, and financial-sector regulations
- Documenting privacy-by-design methodologies
- Maintaining auditability without compromising user data
Testing and Validating Privacy Guarantees
- Testing workflows to prevent unintended data leakage
- Evaluating the balance between accuracy and privacy
- Continuous validation across application updates
Deployment and Maintenance of Privacy-Focused AI Apps
- Managing on-device model updates
- Monitoring performance and compliance over time
- Future-proofing applications for evolving regulations
Summary and Next Steps
Requirements
- Foundational knowledge of mobile or application development
- Practical experience with Python, Kotlin, or Swift
- Basic understanding of AI or machine learning principles
Target Audience
- Enterprise development teams
- Compliance officers
- Developers creating applications handling sensitive data
14 Hours
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