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Duration 21 hours
Course Outline
Foundations of Security in TinyML
- Security challenges within resource-constrained ML systems
- Threat modeling for TinyML deployments
- Risk classification for embedded AI applications
Data Privacy in Edge AI
- Privacy implications of on-device data processing
- Strategies for minimizing data exposure and transfer
- Methods for decentralized data management
Adversarial Attacks on TinyML Models
- Threats related to model evasion and poisoning
- Input manipulation via embedded sensors
- Assessing vulnerabilities in constrained environments
Hardening Embedded ML Systems
- Firmware and hardware protection strategies
- Access control and secure boot protocols
- Best practices for securing inference pipelines
Privacy-Preserving TinyML Techniques
- Quantization and model design focused on privacy
- On-device anonymization techniques
- Lightweight encryption and secure computation methods
Secure Deployment and Maintenance
- Secure provisioning of TinyML devices
- OTA update and patching strategies
- Monitoring and incident response at the edge
Testing and Validation of Secure TinyML Systems
- Security and privacy testing frameworks
- Simulation of real-world attack scenarios
- Validation and compliance assessments
Case Studies and Applied Scenarios
- Security failures in edge AI ecosystems
- Designing resilient TinyML architectures
- Assessing trade-offs between performance and protection
Conclusion and Next Steps
Requirements
- Familiarity with embedded system architectures
- Hands-on experience with machine learning workflows
- Foundational knowledge of cybersecurity principles
Target Audience
- Security analysts
- AI developers
- Embedded engineers
Testimonials (1)
The exercises
