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Duration 14 hours
Course Outline
Introduction to Privacy-Preserving AI
- Fundamental principles of data privacy in mobile applications
- Regulatory factors driving on-device AI adoption
- Advantages and constraints of local processing
Exploring Nano Banana for On-Device Privacy
- Nano Banana model architecture
- Security features and local execution pathways
- Supported platforms and mobile integration patterns
Data Management and Local Processing Methods
- Safely collecting and retaining sensitive data on the device
- Reducing data exposure through local inference
- Strategies for anonymization and pseudonymization
Implementing Privacy-Preserving AI Capabilities
- Creating AI-driven features that do not transmit user data
- Designing workflows compliant with healthcare, finance, or regulatory standards
- Ensuring data isolation between application components
Security Considerations for On-Device Models
- Safeguarding models against extraction or tampering
- Secure sandboxing and permission administration
- Threat modeling for mobile AI systems
Compliance and Regulatory Alignment
- Understanding the impact of GDPR, HIPAA, and financial sector regulations
- Documenting privacy-by-design methodologies
- Maintaining auditability without exposing user data
Testing and Verifying Privacy Assurances
- Testing workflows for accidental data leaks
- Assessing the balance between accuracy and privacy
- Continuous validation throughout application updates
Deployment and Upkeeping of Privacy-Centric AI Apps
- Managing updates for on-device models
- Monitoring performance and compliance over time
- Preparing applications for evolving regulatory landscapes
Summary and Future Directions
Requirements
- Fundamental knowledge of mobile or application development
- Proficiency in Python, Kotlin, or Swift
- Basic awareness of AI or machine learning principles
Target Audience
- Enterprise teams
- Compliance officers
- Developers creating applications handling sensitive data
Testimonials (1)
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