Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to TinyML Security
- Security challenges in resource-constrained ML systems
- Threat models for TinyML deployments
- Risk categories for embedded AI applications
Data Privacy in Edge AI
- Privacy considerations for on-device data processing
- Strategies to minimize data exposure and transfer
- Methods for decentralized data handling
Adversarial Attacks on TinyML Models
- Threats from model evasion and poisoning
- Input manipulation on embedded sensors
- Evaluating vulnerabilities in constrained environments
Hardening Embedded ML Systems
- Firmware and hardware protection layers
- Access control and secure boot mechanisms
- Best practices for safeguarding inference pipelines
Privacy-Preserving TinyML Techniques
- Quantization and model design considerations for privacy
- Methods for on-device anonymization
- Lightweight encryption and secure computation methods
Secure Deployment and Maintenance
- Secure provisioning of TinyML devices
- OTA updates and patching strategies
- Edge-level monitoring and incident response
Testing and Validating Secure TinyML Systems
- Security and privacy testing frameworks
- Simulating real-world attack scenarios
- Validation and compliance considerations
Case Studies and Applied Scenarios
- Security failures in edge AI ecosystems
- Designing resilient TinyML architectures
- Balancing performance and protection trade-offs
Summary and Next Steps
Requirements
- Familiarity with embedded system architectures
- Practical experience with machine learning workflows
- Foundational knowledge of cybersecurity
Target Audience
- Security analysts
- AI developers
- Embedded engineers
Testimonials (1)
The exercises
