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Course Outline
Introduction to Privacy-Preserving Artificial Intelligence
- Core principles of data privacy in mobile applications.
- Regulatory drivers for on-device AI.
- Benefits and limitations of local processing.
Understanding Nano Banana for On-Device Privacy
- Nano Banana model architecture.
- Security properties and local execution paths.
- Supported platforms and mobile integration patterns.
Data Handling and Local Processing Techniques
- Collecting and storing sensitive data securely on-device.
- Minimising data exposure through local inference.
- Anonymisation and pseudonymisation strategies.
Implementing Privacy-Preserving AI Features
- Creating AI-driven features without transmitting user data.
- Designing healthcare-, finance-, or compliance-ready workflows.
- Ensuring data isolation across application components.
Security Considerations for On-Device Models
- Protecting models from extraction or tampering.
- Secure sandboxing and permission management.
- Threat modelling for mobile AI systems.
Compliance and Regulatory Alignment
- Understanding GDPR, HIPAA, and financial-sector implications.
- Documenting privacy-by-design approaches.
- Maintaining auditability without compromising user data.
Testing and Validating Privacy Guarantees
- Testing workflows for unintended data leakage.
- Evaluating accuracy versus privacy trade-offs.
- Continuous validation across application updates.
Deployment and Maintenance of Privacy-Focused AI Applications
- Managing on-device model updates.
- Monitoring performance and compliance over time.
- Future-proofing applications for evolving regulations.
Summary and Next Steps
Requirements
- A foundational understanding of mobile or application development.
- Experience with Python, Kotlin, or Swift.
- Basic familiarity with artificial intelligence or machine learning concepts.
Audience
- Enterprise teams.
- Compliance officers.
- Developers creating applications handling sensitive data.
14 Hours
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