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Course Outline

Introduction to Privacy-Preserving AI

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

Understanding Nano Banana for On-Device Privacy

  • An overview of the Nano Banana model architecture.
  • Security characteristics and local execution pathways.
  • Supported platforms and patterns for mobile integration.

Data Handling and Local Processing Techniques

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

Implementing Privacy-Preserving AI Features

  • Building AI-driven functionalities that do not require transmitting user data externally.
  • Designing workflows suitable for healthcare, finance, or strict compliance environments.
  • Ensuring strict data isolation between different app components.

Security Considerations for On-Device Models

  • Safeguarding models against extraction or unauthorized tampering.
  • Managing secure sandboxing and permission controls.
  • Conducting threat modeling for mobile AI systems.

Compliance and Regulatory Alignment

  • Navigating the implications of GDPR, HIPAA, and financial sector regulations.
  • Documenting privacy-by-design methodologies.
  • Preserving auditability while protecting user data integrity.

Testing and Validating Privacy Guarantees

  • Testing workflows to identify potential data leakage points.
  • Balancing accuracy against privacy trade-offs.
  • Performing continuous validation across application updates.

Deployment and Maintenance of Privacy-Focused AI Apps

  • Overseeing the update process for on-device models.
  • Monitoring long-term performance and compliance status.
  • Preparing applications to adapt to evolving regulatory landscapes.

Summary and Next Steps

Requirements

  • A solid grasp of mobile or general application development principles.
  • Proficiency in Python, Kotlin, or Swift programming languages.
  • A foundational understanding of AI and machine learning concepts.

Target Audience

  • Enterprise technology teams.
  • Compliance officers and legal professionals.
  • Developers responsible for building applications that handle sensitive data.
 14 Hours

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