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Course Outline

Foundations of Privacy-Preserving AI

  • Core principles governing data privacy within mobile applications
  • Regulatory factors driving the adoption of on-device AI solutions
  • Key benefits and inherent limitations of local data processing

Leveraging Nano Banana for On-Device Privacy

  • Overview of the Nano Banana model architecture
  • Examination of security properties and local execution pathways
  • Review of supported platforms and optimal mobile integration patterns

Data Management and Local Processing Strategies

  • Techniques for securely collecting and storing sensitive data on-device
  • Reducing data exposure risks through local inference methods
  • Strategies for effective anonymization and pseudonymization

Building Privacy-Preserving AI Features

  • Developing AI-driven functionalities without the need to transmit user data externally
  • Designing workflows that meet the stringent requirements of healthcare, finance, and compliance sectors
  • Ensuring robust data isolation across various application components

Security Best Practices for On-Device Models

  • Methods for protecting models against extraction or tampering attempts
  • Implementing secure sandboxing and strict permission management
  • Threat modeling techniques specifically for mobile AI ecosystems

Aligning with Compliance and Regulatory Standards

  • Understanding the implications of GDPR, HIPAA, and financial sector regulations
  • Documenting privacy-by-design methodologies for regulatory transparency
  • Preserving auditability while strictly safeguarding user data

Testing and Verifying Privacy Guarantees

  • Conducting tests to identify and prevent unintended data leakage
  • Balancing and evaluating the trade-offs between model accuracy and privacy
  • Performing continuous validation checks across application updates

Deploying and Maintaining Privacy-Centric AI Applications

  • Managing the update lifecycle for on-device models
  • Monitoring long-term performance and ongoing compliance status
  • Preparing applications to adapt to evolving regulatory landscapes

Course Summary and Path Forward

Requirements

  • A solid foundation in mobile or application development principles
  • Working experience with Python, Kotlin, or Swift
  • Foundational knowledge of AI or machine learning concepts

Target Audience

  • Enterprise technology teams
  • Compliance and governance officers
  • Developers responsible for building security-sensitive applications
 14 Hours

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