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Duration 14 hours
Course Outline
Fundamentals of Privacy-Preserving AI
- Essential data privacy principles within mobile application development
- Regulatory factors driving the adoption of on-device AI
- The advantages and constraints associated with local processing
Exploring Nano Banana for On-Device Privacy
- Architectural overview of the Nano Banana model
- Security characteristics and local execution mechanisms
- Compatible platforms and patterns for mobile integration
Data Management and Local Processing Methods
- Securing the collection and storage of sensitive data on the device
- Reducing data exposure through local inference techniques
- Strategies for anonymization and pseudonymization
Building Privacy-Preserving AI Capabilities
- Creating AI-driven features that do not require user data transmission
- Designing workflows suitable for healthcare, finance, or strict compliance requirements
- Safeguarding data isolation between various app components
Security Perspectives for On-Device Models
- Defending models against extraction attempts or tampering
- Managing secure sandboxing and permissions
- Conducting threat modeling for mobile AI systems
Alignment with Compliance and Regulatory Standards
- Navigating the implications of GDPR, HIPAA, and financial sector regulations
- Documenting privacy-by-design methodologies
- Ensuring auditability without compromising user data integrity
Testing and Verifying Privacy Assurances
- Testing workflows to detect unintended data leaks
- Assessing the balance between accuracy and privacy
- Performing continuous validation throughout app updates
Deployment and Ongoing Maintenance of Privacy-Centric AI Apps
- Oversight of on-device model updates
- Long-term monitoring of performance and compliance status
- Preparing applications for future regulatory changes
Recap and Future Directions
Requirements
- A foundational understanding of mobile or application development principles
- Proficiency in programming languages such as Python, Kotlin, or Swift
- A basic grasp of artificial intelligence and machine learning concepts
Target Audience
- Enterprise teams
- Compliance officers
- Developers responsible for building sensitive applications
Testimonials (1)
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