Cyber-physical systems (CPSs) and the Internet of Things (IoT) are increasingly important technologies within all sectors of society. Information security of the systems, including confidentiality, integrity, and availability, is of utmost importance, and violations may have severe consequences, from harming humans to damage to property or the environment. This project addresses privacy and security-related problems from a physical-layer perspective, where conventional methods fail to detect security breaches and privacy invasions. The issues considered are challenging because of the distributed nature of CPS/IoT, the dynamic nature of attacks, and infrastructure constraints. Furthermore, the existing data security approaches neglect the additional information available at the physical layer, such as sensor redundancy, control laws, and physical processes. The project embraces a secure-by-design philosophy for designing CPS and IoT with security and privacy as an inherent aspect of the functionality without depending on additional mechanisms later in the design. This ensures that CPS and IoT function even when other security protocols fail and maintain operations under adversarial conditions.
We have developed new privacy-preserving schemes; in particular, we have designed distributed machine-learning algorithms that feature new privacy metrics and privacy-by-design. In addition, we have extended our initial results on communication-efficient federated learning suitable for streaming data. In federated learning, the aggregator only receives model updates from a set of participants and has no visibility into the data used for the learning. Hence, malicious participants can perform model poisoning attacks to control the model predictions for select data points. In the last year, we have broadened our study of federated learning to include the effects and countermeasures of natural and artificial disturbances, e.g., channel noise, privacy leakage, and model poisoning. We have also initiated studies on the challenges of federated learning in medical applications.
Technology innovation has the potential to introduce new technologies that could support people and society during these difficult times. Therefore, under the COPS project, we propose using drones as a companion to tackle current COVID-19 and future pandemics. The proposed COROID drone is based on crowdsourcing sensor data of the public's intelligent devices, which can correlate with the reading of the infrared cameras equipped on COROID drones.
Next, under the COPS project, we propose and investigate a bidirectional device-to-device (D2D) transmission scheme that exploits cooperative downlink non-orthogonal multiple access (NOMA) (termed BCD-NOMA). The BCD-NOMA model can support peer-to-peer content sharing, multiplayer gaming, social networking, and so on. The security and privacy of such applications are of great interest. Therefore, as a starting point, we study in detail the performance of the BCD-NOMA scheme. Unlike previous works, BCD-NOMA is designed for high ergodic capacity (EC) and high energy efficiency.
This medium-term time horizon research project develops advanced inference and optimization approaches to overcome challenges faced by future cyber physical systems/internet-of-things (CPS/IoT) and enable a sustainable and resilient digital society. The project addresses privacy and security related problems from a physical-layer perspective where traditional methods fail to detect security breaches and privacy invasions. The considered problems are particularly challenging due to the distributed nature of CPS/IoT, dynamic and non-stationary nature of attacks, and infrastructure constraints. The existing data security approaches ignore the additional information available at the physical layer such as sensor redundancy, control laws, and physical processes. Leveraging on the model knowledge and side information, the project adopts a secure-by-design philosophy for designing CPS and IoT with security and privacy as an inherent aspect of the functionality without solely depending on additional mechanisms at a later part of the design. Unlike the current approaches that assume security and privacy as an additional feature of the system, it is here consider to be a fundamental design constraint. This ensures that CPS and IoT function even when other security protocols fail and maintain operations under adversarial conditions. Finally, the project aims to strengthen the existing liaisons between academia and industry (NTNU and SINTEF), foster international scientific and education collaboration to educate and inform a new generation of scientists and engineers in the strategic area of IoT technologies. International co-operation with world-class research universities forms an important part of the project in the form of researcher exchange.