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Course Outline

Introduction to Privacy-Centric AI

  • Fundamental principles of data privacy within mobile applications.
  • Regulatory factors driving the shift toward on-device AI.
  • Advantages and constraints of local data processing.

Grasping Nano Banana for On-Device Privacy

  • Overview of the Nano Banana model architecture.
  • Security features and local execution pathways.
  • Compatible platforms and mobile integration patterns.

Data Management and Local Processing Strategies

  • Securely collecting and storing sensitive data on the device.
  • Reducing data exposure through local inference capabilities.
  • Strategies for anonymization and pseudonymization.

Building Privacy-Preserving AI Features

  • Developing AI-driven features that avoid transmitting user data.
  • Designing workflows compliant with healthcare, finance, or general regulatory standards.
  • Ensuring data isolation between various app components.

Security Aspects for On-Device Models

  • Safeguarding models against extraction or tampering.
  • Secure sandboxing and effective permission management.
  • Threat modeling specifically for mobile AI systems.

Regulatory Compliance and Alignment

  • Navigating the implications of GDPR, HIPAA, and financial sector regulations.
  • Documenting privacy-by-design methodologies.
  • Maintaining audit trails without compromising user data integrity.

Testing and Verifying Privacy Assurance

  • Testing workflows to detect unintended data leaks.
  • Balancing and evaluating the trade-offs between accuracy and privacy.
  • Implementing continuous validation throughout app updates.

Deploying and Maintaining Privacy-Focused AI Apps

  • Managing updates for on-device models.
  • Monitoring long-term performance and compliance status.
  • Future-proofing applications to adapt to evolving regulations.

Conclusion and Recommended Next Steps

Requirements

  • A foundational understanding of mobile or general application development.
  • Proficiency in Python, Kotlin, or Swift.
  • Basic knowledge of AI or machine learning concepts.

Target Audience

  • Enterprise development teams.
  • Compliance and regulatory officers.
  • Developers working on sensitive or high-security applications.
 14 Hours

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