Security and Privacy in TinyML Applications Training Course

Introduction to Security in TinyML for Government

• Security challenges in resource-constrained machine learning systems
• Threat models for TinyML deployments in government operations
• Risk categories for embedded artificial intelligence applications in the public sector

Data Privacy in Edge AI for Government

• Privacy considerations for on-device data processing in governmental systems
• Strategies for minimizing data exposure and transfer within government networks
• Techniques for decentralized data handling to enhance privacy for government applications

Adversarial Attacks on TinyML Models for Government

• Threats of model evasion and poisoning in government deployments
• Risks associated with input manipulation on embedded sensors used by government agencies
• Methods for assessing vulnerability in resource-constrained environments within the public sector

Security Hardening for Embedded ML for Government

• Implementation of firmware and hardware protection layers for government devices
• Access control and secure boot mechanisms to ensure integrity in government systems
• Best practices for safeguarding inference pipelines in governmental applications

Privacy-Preserving TinyML Techniques for Government

• Quantization and model design considerations for enhancing privacy in government use cases
• Techniques for on-device anonymization to protect sensitive information
• Lightweight encryption and secure computation methods suitable for government applications

Secure Deployment and Maintenance for Government

• Secure provisioning of TinyML devices in governmental networks
• Over-the-air (OTA) updates and patching strategies to maintain security in government systems
• Monitoring and incident response protocols at the edge for effective governance

Testing and Validation of Secure TinyML Systems for Government

• Security and privacy testing frameworks tailored for governmental use
• Simulating real-world attack scenarios to assess system resilience in government applications
• Validation and compliance considerations for ensuring regulatory adherence in government operations

Case Studies and Applied Scenarios for Government

• Security failures in edge AI ecosystems within the public sector
• Designing resilient TinyML architectures for government use
• Evaluating trade-offs between performance and protection in governmental applications

Summary and Next Steps for Government