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Duration 14 hours
Course Outline
Introduction to Privacy-Preserving AI
- Key principles of data privacy in mobile applications
- Regulatory factors driving the adoption of on-device AI
- Advantages and constraints of local processing
Comprehending Nano Banana for On-Device Privacy
- Architecture of the Nano Banana model
- Security features and local execution pathways
- Supported platforms and mobile integration patterns
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
Implementing Privacy-Preserving AI Features
- Developing AI-driven features without transmitting user data externally
- Designing workflows suited for healthcare, finance, or compliance contexts
- Ensuring data isolation between different app components
Security Considerations for On-Device Models
- Safeguarding models against extraction or tampering
- Secure sandboxing and permission management
- Threat modeling for mobile AI systems
Compliance and Regulatory Alignment
- Understanding the implications of GDPR, HIPAA, and financial sector regulations
- Documenting privacy-by-design methodologies
- Maintaining auditability without compromising user data integrity
Testing and Verifying Privacy Assurances
- Testing workflows to detect any unintended data leakage
- Assessing the balance between accuracy and privacy
- Continuous validation across application updates
Deployment and Maintenance of Privacy-Focused AI Apps
- Managing updates for on-device models
- Monitoring performance and compliance over time
- Future-proofing applications against evolving regulatory landscapes
Summary and Next Steps
Requirements
- Familiarity with mobile or application development
- Proficiency in Python, Kotlin, or Swift
- Basic knowledge of AI or machine learning principles
Target Audience
- Enterprise teams
- Compliance officers
- Developers creating sensitive applications
Testimonials (1)
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