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Duration 14 hours
Course Outline
Introduction to Privacy-Preserving AI
- Core principles governing data privacy within mobile applications.
- Regulatory factors driving the adoption of on-device AI.
- Key benefits and inherent limitations of local processing models.
Understanding Nano Banana for On-Device Privacy
- Overview of the Nano Banana model architecture.
- Security properties and local execution pathways.
- Supported platforms and standard mobile integration patterns.
Data Handling and Local Processing Techniques
- Secure collection and storage of sensitive data directly on the device.
- Strategies to minimize data exposure through local inference.
- Techniques for anonymization and pseudonymization.
Implementing Privacy-Preserving AI Features
- Creating AI-driven features that operate without transmitting user data.
- Designing workflows tailored for healthcare, finance, or other compliance-heavy sectors.
- Ensuring strict data isolation across different application components.
Security Considerations for On-Device Models
- Methods to protect models from extraction or tampering.
- Implementation of secure sandboxing and permission management.
- Threat modeling specifically for mobile AI systems.
Compliance and Regulatory Alignment
- Navigating the implications of GDPR, HIPAA, and financial-sector regulations.
- Documenting privacy-by-design approaches effectively.
- Maintaining auditability while safeguarding user data integrity.
Testing and Validating Privacy Guarantees
- Testing workflows to detect and prevent unintended data leakage.
- Evaluating the balance between accuracy and privacy trade-offs.
- Implementing continuous validation processes across app updates.
Deployment and Maintenance of Privacy-Focused AI Apps
- Managing updates for on-device models.
- Monitoring long-term performance and compliance status.
- Designing applications to remain adaptable to evolving regulatory landscapes.
Summary and Next Steps
Requirements
- A foundational understanding of mobile or general application development.
- Practical experience working with Python, Kotlin, or Swift.
- Basic familiarity with core AI or machine learning concepts.
Target Audience
- Enterprise development teams.
- Compliance officers and governance professionals.
- Developers responsible for building applications that handle sensitive data.
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