A Data Space is a (potentially decentralized) entity representing regional or organizational units within a city that share common environmental characteristics, focus on a specific data domain, and are managed for a defined set of purposes. Within such data spaces, information collected by citizens' IoT and personal devices, as well as by platforms gathering relevant citizen data, can significantly contribute to improving safety, environmental sustainability, and overall wellbeing. These benefits arise through the enhancement of applications such as smart home management, household energy usage optimization, waste collection management, and urban mobility services.
Data Spaces provide the trusted, interoperable, and sovereign data infrastructure upon which AI-based applications can be safely trained, deployed, and continuously improved. By enabling controlled data sharing across organizational and administrative boundaries, Data Spaces form a key enabler for scalable, data-driven intelligence in smart cities and urban environments.
However, the federated and multi-stakeholder nature of Data Spaces also introduces new and critical AI-oriented cybersecurity challenges. Threats such as data poisoning, adversarial manipulation, model extraction, inference-based privacy leakage, and attacks on collaborative learning pipelines directly undermine the reliability, safety, and trustworthiness of AI-based services operating on shared data. Addressing these challenges requires AI-specific security mechanisms that are tightly integrated into Data Space architectures, governance models, and data management workflows.
This workshop aims to bring together researchers and practitioners from mobile data management, artificial intelligence, cybersecurity, and smart city systems to explore novel approaches, architectures, and methodologies for building secure, trustworthy, and intelligent Data Spaces.

Topics of interest

The workshop topics include, but are not limited to, the following:
- Data Spaces for smart cities and urban environments
- Mobile and IoT data management in federated Data Spaces
- AI-enabled services built on Data Spaces
- Collaborative Machine Learning and Federated Learning across Data Spaces
- Secure data sharing, governance, and data sovereignty mechanisms
- AI-oriented cybersecurity threats in Data Spaces
- Privacy-preserving AI techniques for Data Spaces (e.g., differential privacy, secure aggregation)
- Trust, explainability, and accountability of AI in Data Spaces
- Secure and resilient data pipelines for mobile and edge environments
- Compliance with regulatory frameworks (e.g., GDPR, AI governance)
- Real-world deployments, use cases, and lessons learned
- Runtime monitoring, continuous assurance, and lifecycle security of AI models
- AI supply-chain security and model provenance in Data Spaces
- Federated learning, adversarial robustness and secure Federated Learning for Data Spaces in the Cloud/Edge Continuum