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Duration 14 hours
Course Outline
Foundations of Privacy-Preserving AI
- Key principles of data privacy within mobile application ecosystems
- Regulatory factors driving the shift toward on-device AI
- Advantages and constraints associated with local data processing
Exploring Nano Banana for On-Device Privacy
- Architectural components of the Nano Banana framework
- Security features and local execution pathways
- Compatible platforms and standard mobile integration patterns
Strategies for Data Handling and Local Processing
- Secure collection and storage of sensitive data on mobile devices
- Reducing data exposure through local inference capabilities
- Application of anonymization and pseudonymization techniques
Building Privacy-Centric AI Features
- Developing AI-driven functionalities without transmitting user data externally
- Designing workflows suitable for healthcare, finance, and compliance-critical sectors
- Maintaining strict data isolation across different application modules
Security Protocols for On-Device Models
- Safeguarding models against extraction attempts or tampering
- Implementing secure sandboxing and effective permission management
- Conducting threat modeling specific to mobile AI systems
Aligning with Compliance and Regulatory Standards
- Navigating GDPR, HIPAA, and other financial sector regulatory implications
- Documenting privacy-by-design methodologies for audit purposes
- Ensuring auditability while preserving the confidentiality of user data
Testing and Verifying Privacy Assurances
- Testing workflows to detect any unintended data leakage
- Assessing the balance between model accuracy and privacy preservation
- Establishing continuous validation processes throughout application updates
Deploying and Maintaining Privacy-Focused AI Applications
- Managing updates for on-device models effectively
- Monitoring long-term performance and regulatory compliance
- Future-proofing applications to adapt to evolving regulatory landscapes
Conclusion and Future Directions
Requirements
- Fundamental knowledge of mobile or application development practices
- Proficiency in at least one of the following languages: Python, Kotlin, or Swift
- Basic understanding of core AI and machine learning concepts
Target Audience
- Enterprise technology teams
- Compliance and risk management officers
- Developers responsible for building applications handling sensitive data
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