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Course Outline

Introduction to Privacy-Centric AI

  • Foundational principles of data privacy within mobile applications.
  • Regulatory factors driving the adoption of on-device AI.
  • Advantages and constraints associated with local data processing.

Comprehending Nano Banana for On-Device Privacy

  • Overview of Nano Banana's model architecture.
  • Security characteristics and local execution mechanisms.
  • Compatible platforms and standard mobile integration approaches.

Data Management and Local Processing Strategies

  • Secure collection and storage of sensitive data on the device.
  • Reducing data exposure through local inference capabilities.
  • Strategies for anonymization and pseudonymization.

Implementing Privacy-Preserving AI Capabilities

  • Developing AI-driven features that do not require user data transmission.
  • Designing workflows suitable for healthcare, finance, or compliance-critical sectors.
  • Safeguarding data isolation between different application components.

Security Considerations for On-Device Models

  • Defending models against extraction or tampering attempts.
  • Implementing secure sandboxing and rigorous permission controls.
  • Threat modeling specific to mobile AI systems.

Regulatory Compliance and Alignment

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

Testing and Verification of Privacy Assurances

  • Identifying potential workflows for unintended data leakage.
  • Balancing accuracy against privacy trade-offs.
  • Performing continuous validation across application updates.

Deployment and Maintenance of Privacy-Focused AI Applications

  • Managing updates for on-device models.
  • Tracking performance and compliance metrics over time.
  • Ensuring application longevity in the face of evolving regulations.

Wrap-Up and Future Directions

Requirements

  • Foundational knowledge of mobile or application development.
  • Proficiency in Python, Kotlin, or Swift.
  • Basic comprehension of AI or machine learning principles.

Target Audience

  • Enterprise development teams.
  • Compliance officers and governance professionals.
  • Developers creating applications handling sensitive data.
 14 Hours

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