Towards Privacy Preserving Data Provenance for the Internet of Things

As the Internet of Things evolves, security and privacy aspects are becoming the main barriers in the development of innovative and valuable services that will transform our society. One of the biggest challenges in IoT lies in the design of secure and privacy-preserving solutions guaranteeing privacy properties such as anonymity, unlinkability, minimal disclosure of personally identifiable information, as well as assuring security properties, such as content integrity and authenticity. In this regard, this paper provides a data provenance solution that meets those properties, enabling a privacy-preserving identity auditing of the IoT sensor's exchanged data, whereas allowing de-anonymization of the real owner identity of the associated IoT shared data in case of law enforcement inspection is needed, (e.g. identity theft or related cyber-crimes). This research is built on the foundations of the ARIES European identity ecosystem for highly secure and privacy-respecting physical and virtual identity management processes.