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Duration 21 hours
Course Outline
Foundations of Security in TinyML
- Security challenges inherent in resource-limited ML systems
- Threat models specific to TinyML implementations
- Risk categories affecting embedded AI applications
Data Privacy in Edge AI
- Privacy implications of on-device data processing
- Strategies for reducing data exposure and transfer
- Methods for decentralized data management
Defending TinyML Models Against Adversarial Attacks
- Threats from model evasion and data poisoning
- Manipulation of inputs on embedded sensors
- Evaluating vulnerabilities within constrained environments
Hardening Security for Embedded ML
- Firmware and hardware protection layers
- Access control protocols and secure boot mechanisms
- Optimal practices for protecting inference pipelines
Privacy-Preserving Techniques for TinyML
- Quantization and model design considerations for privacy
- On-device anonymization methods
- Lightweight encryption and secure computation approaches
Secure Deployment and Maintenance
- Secure provisioning processes for TinyML devices
- OTA update and patching strategies
- Edge-level monitoring and incident response
Testing and Validating Secure TinyML Systems
- Frameworks for security and privacy testing
- Simulating real-world attack vectors
- Compliance and validation considerations
Case Studies and Applied Scenarios
- Security breaches in edge AI ecosystems
- Architecting resilient TinyML systems
- Balancing performance with security protections
Conclusion and Future Directions
Requirements
- Familiarity with embedded system architectures
- Hands-on experience with machine learning workflows
- Fundamental knowledge of cybersecurity principles
Target Audience
- Security Analysts
- AI Developers
- Embedded Engineers
Testimonials (1)
The exercises
