This workshop aims to foster discussions on research and developments in secure, privacy-preserving, and trust-based architectures for next-generation IoT systems. We invite contributions that explore models, protocols, algorithms, services, and applications designed to address the security and privacy challenges in IoT. We particularly encourage submissions that leverage innovative machine learning techniques to manage the issues of data volume and variety that are inherent in IoT platforms.

We plan to seek previously unpublished work in theoretical or experimental research, or work in-progress on topics including, but not limited to, the following:

· Architectures and protocols for secure, robust and privacy enhancing IoT
· Security and privacy frameworks for IoT
· Cryptographic approaches for security and privacy in IoT
· Trust frameworks and management models for IoT
· Wireless security protocols for IoT
· Threat and attack models in IoT
· Intrusion and malware detection for IoT
· Security and Privacy in AI-driven IoT Systems
· Machine learning for anomaly detection in IoT
· Privacy-preserving Edge Machine Learning in IoT
· Privacy enhancing and anonymization techniques in IoT
· Security Using Federated Learning in IoT Systems
· Adversarial attacks against Federated Learning in IoT Systems
· Blockchain algorithms and applications for IoT security and privacy
· Security and privacy issues posed by LLMs in IoT

Important Dates
Papers submission: March 1, 2026
Notification of acceptance: April 29, 2026
Workshop date: July 29-30, 2026