The deployment of IoT systems are increasing worldwide, but so does security incidents like ransomware and cyber-extortion, which are not sufficiently covered by current cybersecurity practices and will proliferate with upcoming technologies like 5G. At the same pace, attacks are becoming more sophisticated, requiring reinforced cybersecurity competencies, as well as more effective mechanisms for detection and mitigation of attacks. ARCADIAN-IoT established the goal to develop and make available an innovative, advanced framework for trust, security and privacy management in IoT systems to promote trustworthy IoT systems. ARCADIAN-IoT will accelerate the development towards decentralized, transparent and user controllable privacy in three real use cases: Emergency and vigilance using drones and IoT; Monitoring of grid infrastructure; and medical IoT devices for telemonitoring and follow-up of cancer patients. ARCADIAN-IoT aims to fill a gap in the IoT threat intelligence landscape by providing a novel IoT- specific Cyber Threat Intelligence (CTI) approach, which supports integrated, automated and privacy- preserved IoT threat information sharing. ARCADIAN-IoT will research distributed ledger technologies to enable decentralized trust management and will leverage on federated AI for effective cybersecurity solutions complying with privacy regulations. ARCADIAN-IoT will act as an enabler of IoT systems in future networks by including eSIM technology with over the air management support for devices, and to enhance identity management through trusted hardware – Root of Trust (RoT). ARCADIAN-IoT will enable a Chain of Trust (CoT) to address identity, trust and recovery considering the multiple entities in IoT systems: persons, IoT devices/objects and Apps/services. At last, ARCADIAN-IoT will enhance cybersecurity awareness of stakeholders through a cybersecurity training package.

Data analytics is one of the main cornerstones in many enterprise architectures and the data lake paradigm is more and more adopted to assist organizations in taking reliable, accurate, and fast decisions. Although the initial approaches to address these issues saw the data lakes as the evolution of data warehouses to be implemented on-premises, cloud providers are nowadays including in their offerings platforms able to setup and run them. Nevertheless, the increasing amount of data generated at the edge and the need to enable the data sharing among organizations are posing new challenges in terms of performances, energy efficiency, and privacy/confidentiality which can be properly addressed with data lakes which are deployed along the whole computing continuum as well as building a federation of such data lakes. The ambition of TEADAL is to provide key cornerstone technologies to create stretched data lakes spanning the cloud-edge continuum and multi-cloud, providing privacy, confidentiality, and energy-efficient data management. The TEADAL data lake technologies will enable trusted, verifiable and energy efficient data flows, both in a stretched data lake and across a trustworthy mediatorless federation of them, based on a shared approach for defining, enforcing, and tracking privacy/confidentiality requirements balanced with the need for energy reduction.