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Course Outline
Foundations of Privacy-Preserving AI
- Fundamental data privacy principles within mobile applications.
- Regulatory factors driving the adoption of on-device AI.
- The advantages and inherent limitations of local data processing.
Navigating Nano Banana for On-Device Privacy
- Overview of the Nano Banana model architecture.
- Security attributes and local execution pathways.
- Supported platforms and best practices for mobile integration.
Strategies for Secure Data Handling and Local Processing
- Secure collection and storage of sensitive data directly on the device.
- Reducing data exposure through local inference mechanisms.
- Implementation of anonymization and pseudonymization strategies.
Deploying Privacy-Preserving AI Functionality
- Developing AI-driven features that operate without external data transmission.
- Architecting workflows suitable for healthcare, finance, or compliance-critical contexts.
- Ensuring robust data isolation across various application components.
Security Imperatives for On-Device Models
- Safeguarding models against extraction or malicious tampering.
- Effective secure sandboxing and granular permission management.
- Applying threat modeling techniques specific to mobile AI systems.
Aligning with Compliance and Regulatory Standards
- Navigating the implications of GDPR, HIPAA, and sector-specific financial regulations.
- Documenting privacy-by-design methodologies.
- Maintaining auditability while strictly protecting user data integrity.
Verifying and Validating Privacy Assurances
- Conducting tests to identify and prevent unintended data leakage.
- Assessing the balance between model accuracy and privacy preservation.
- Ensuring continuous validation across successive application updates.
Rolling Out and Sustaining Privacy-Focused AI Applications
- Managing the lifecycle of on-device model updates.
- Continuously monitoring performance metrics and compliance status.
- Future-proofing applications to adapt to evolving regulatory landscapes.
Conclusion and Path Forward
Requirements
- A foundational understanding of mobile or application development principles.
- Practical experience with Python, Kotlin, or Swift.
- Basic familiarity with core AI or machine learning concepts.
Target Audience
- Enterprise technical teams.
- Compliance and regulatory officers.
- Developers responsible for building security-sensitive applications.
14 Hours
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
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