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Duration 21 hours
Course Outline
Foundations of TinyML Security
- Security hurdles in resource-limited ML systems
- Threat modeling for TinyML implementations
- Risk classification for embedded AI solutions
Data Privacy in Edge Intelligence
- Privacy implications of on-device data processing
- Strategies to minimize data exposure and transmission
- Methods for decentralized data management
Countering Adversarial Attacks on TinyML
- Evasion and model poisoning risks
- Input tampering on embedded sensor arrays
- Vulnerability assessment in constrained settings
Hardening Embedded ML Systems
- Firmware and hardware security layers
- Access control and secure boot protocols
- Best practices for protecting inference pipelines
Privacy-Centric TinyML Methods
- Quantization and architectural design for privacy
- On-device anonymization techniques
- Lightweight encryption and secure computation approaches
Secure Deployment & Lifecycle Management
- Safe provisioning of TinyML hardware
- Over-the-air (OTA) updates and patch management
- Edge-based monitoring and incident response
Testing & Validation of Secure TinyML
- Security and privacy testing frameworks
- Simulation of real-world attack vectors
- Compliance and validation requirements
Case Studies & Practical Applications
- Security breaches in edge AI ecosystems
- Designing robust TinyML architectures
- Balancing performance with protective measures
Wrap-up & Future Directions
Requirements
- Familiarity with embedded system architectures
- Proficiency in machine learning processes
- Foundational knowledge of cybersecurity principles
Intended Audience
- Security Specialists
- AI Engineers
- Embedded Systems Architects
Testimonials (1)
The exercises
