A Toolchain and Interoperability Framework to enhance privacy and individual control at the Edge

TL;DR: In this paper , the authors present five key pillars of innovation towards privacy-preserving edge computing, regarding smart sampling of IoT devices, anonymous authentication and consent management, dynamic data-driven pattern management, opportunistic IoT clustering, distributed IoT data governance, and resource integrity validation.

Abstract: Internet-of-Things (IoT) has the potential to create new services and markets by allowing the exploration of new, often completely different ways of doing things, based on the clustering and aggregation of data from different sources and fields of activity. As technology advances, new ethical, legal, and technological concerns arise. In this paper, we present five key pillars of innovation towards privacy-preserving edge computing, regarding smart sampling of IoT devices, anonymous authentication and consent management, dynamic data-driven pattern management, opportunistic IoT clustering, distributed IoT data governance, and resource integrity validation. The overall concept of this paper, is to create a comprehensive methodological framework and toolset for definition, deployment and operation of privacy-compliant IoT platforms tailored to specific use-cases. During this process we are not creating “yet another IoT platform”., but rather building upon past efforts to the maximum extent possible. This approach takes into consideration existing solutions in the following areas: high-level concepts and standards; integration and interoperability frameworks; IoT platforms and infrastructural elements.