문의를 보내주셔서 감사합니다! 팀원이 곧 연락드리겠습니다.
예약을 보내주셔서 감사합니다! 저희 팀 멤버 중 한 분이 곧 연락드리겠습니다.
코스 개요
Introduction to Privacy-Preserving AI
- Core principles of data privacy in mobile applications
- Regulatory drivers for on-device AI
- Benefits and limitations of local processing
Understanding Nano Banana for On-Device Privacy
- Nano Banana model architecture
- Security properties and local execution paths
- Supported platforms and mobile integration patterns
Data Handling and Local Processing Techniques
- Collecting and storing sensitive data securely on-device
- Minimizing data exposure using local inference
- Anonymization and pseudonymization strategies
Implementing Privacy-Preserving AI Features
- Creating AI-driven features without transmitting user data
- Designing healthcare-, finance-, or compliance-ready workflows
- Ensuring data isolation across app components
Security Considerations for On-Device Models
- Protecting models from extraction or tampering
- Secure sandboxing and permission management
- Threat modeling for mobile AI systems
Compliance and Regulatory Alignment
- Understanding GDPR, HIPAA, and financial-sector implications
- Documenting privacy-by-design approaches
- Maintaining auditability without compromising user data
Testing and Validating Privacy Guarantees
- Testing workflows for unintended data leakage
- Evaluating accuracy vs privacy trade-offs
- Continuous validation across app updates
Deployment and Maintenance of Privacy-Focused AI Apps
- Managing on-device model updates
- Monitoring performance and compliance over time
- Future-proofing applications for evolving regulations
Summary and Next Steps
요건
- An understanding of mobile or application development
- Experience with Python, Kotlin, or Swift
- Basic familiarity with AI or machine learning concepts
Audience
- Enterprise teams
- Compliance officers
- Developers building sensitive applications
14 시간
회원 평가 (1)
Flow , vibe and topic on presentation
