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Course Outline
Foundations of Privacy-Preserving AI
- Fundamental data privacy principles in mobile contexts
- Regulatory factors driving the adoption of on-device AI
- Advantages and constraints of local data processing
Nano Banana for Enhanced On-Device Privacy
- Architectural overview of Nano Banana
- Security features and local execution mechanisms
- Compatible platforms and mobile integration strategies
Secure Data Management and Local Processing
- Secure collection and storage of sensitive data on-device
- Reducing data exposure through local inference
- Strategies for anonymization and pseudonymization
Building Privacy-First AI Features
- Developing AI functionalities without external data transmission
- Designing workflows suitable for healthcare, finance, or strict compliance requirements
- Safeguarding data isolation between application modules
Security Posture for On-Device Models
- Defending models against extraction or manipulation
- Implementing secure sandboxing and controlling permissions
- Threat modeling for mobile AI ecosystems
Regulatory Compliance and Alignment
- Navigating GDPR, HIPAA, and financial industry mandates
- Recording privacy-by-design methodologies
- Preserving audit trails while protecting user data integrity
Verifying Privacy Assurances
- Testing for potential data leakage in workflows
- Balancing accuracy against privacy considerations
- Ongoing verification through application updates
Rollout and Sustenance of Privacy-Focused AI Applications
- Overseeing updates to on-device models
- Tracking performance and compliance metrics over time
- Preparing applications for future regulatory changes
Wrap-Up and Future Directions
Requirements
- Familiarity with mobile or application development workflows
- Proficiency in Python, Kotlin, or Swift
- Fundamental grasp of AI or machine learning principles
Target Audience
- Corporate technology teams
- Compliance specialists
- Engineers developing sensitive or confidential applications
14 Hours
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