Secure and Transparent Data Sharing Among Connected Devices: Integrating Data Spaces and Provenance

Partnerships among stakeholders involved with con-nected devices often need to exchange sensitive data, raising significant security and privacy concerns. Traditional methods reliant solely on platforms such as the Malware Information Sharing Platform may pose limitations in addressing these challenges. This paper introduces a novel framework supporting secure data sharing among connected loT devices, leveraging data spaces and data provenance concepts. The proposed framework integrates data spaces to facilitate secure data exchange and incorporates data provenance to trace the origins and trans-formations of shared data, ensuring accountability and trans-parency. The framework has practical applications in contexts such as structural health monitoring, where managing data from loT devices, sensors, and data feeds enables data and threat sharing among different structures. Our approach aims to mitigate security risks and privacy breaches while increasing trust among participating entities.