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Course Outline

Introduction to Privacy-Preserving AI

  • Fundamental principles of data privacy in mobile applications
  • Regulatory drivers necessitating on-device AI
  • Advantages and constraints of local processing

Understanding Nano Banana for On-Device Privacy

  • Nano Banana model architecture
  • Security properties and local execution pathways
  • Supported platforms and mobile integration patterns

Data Handling and Local Processing Techniques

  • Securely collecting and storing sensitive data on-device
  • Reducing data exposure through local inference
  • Anonymization and pseudonymization strategies

Implementing Privacy-Preserving AI Features

  • Creating AI-driven features without transmitting user data
  • Designing workflows suitable for healthcare, finance, or compliance
  • Ensuring data isolation across application components

Security Considerations for On-Device Models

  • Safeguarding models from extraction or tampering
  • Secure sandboxing and permission management
  • Threat modeling for mobile AI systems

Compliance and Regulatory Alignment

  • Understanding the implications of GDPR, HIPAA, and financial-sector regulations
  • Documenting privacy-by-design approaches
  • Maintaining auditability without compromising user data

Testing and Validating Privacy Guarantees

  • Testing workflows to prevent unintended data leakage
  • Evaluating the trade-offs between accuracy and privacy
  • Continuous validation across app updates

Deployment and Maintenance of Privacy-Focused AI Apps

  • Managing on-device model updates
  • Monitoring performance and compliance over time
  • Future-proofing applications for evolving regulations

Summary and Next Steps

Requirements

  • A solid grasp of mobile or application development
  • Proficiency in Python, Kotlin, or Swift
  • A basic understanding of AI or machine learning concepts

Target Audience

  • Enterprise teams
  • Compliance officers
  • Developers creating sensitive applications
 14 Hours

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