The prevalence of social robots is increasing, with examples such as customer service robots in malls and airports. This trend highlights the importance of transparency, particularly in data-sharing interactions with social robots operating in public spaces, where users may be asked to provide personal information to receive personalized experiences. This article investigates how design transparency influences user trust and data-sharing behavior in human-robot interactions. We conducted an experiment with 143 participants who interacted with the social robot ARI under two transparency conditions: low and high transparency. In the low-transparency condition, participants were informed about the data being collected and could choose to save or delete it. In the high-transparency condition, the robot additionally indicated the sensitivity level of each data item: low (e.g., scenario preference), medium (e.g., name and e-mail), and high (e.g., religious beliefs), allowing participants to make more informed decisions. Participants were presented with two scenarios: exploring city events and discovering local attractions. They received personalized recommendations based on their preferences, with the option to provide personal data (name, phone number, e-mail) for possible future communication. After the interaction, participants decided whether to save or delete the data they had shared. The results indicated that while transparency did not significantly affect trust in the robot, it influenced data-sharing behavior. In particular, participants in the high-transparency condition demonstrated more cautious behavior, opting to save less data and delete more. Furthermore, the results showed that both sensitivity levels and transparency influenced the participants’ data-sharing choices. Low-level sensitivity data led to the highest rates of saving and the lowest rates of deleting, while medium-level sensitivity data showed the opposite pattern. These findings highlight the need to align data categorization with user perceptions to address data sharing concerns more effectively.

Federated decentralized IoT systems are reshaping how data is exchanged and processed across domains such as smart cities and healthcare, yet ensuring trust in such dynamic, distributed environments remains a significant challenge. This paper introduces FedTrust, a layered framework that integrates decentralized communication, federated data services, and a self-adaptive trust-risk model to assess the trustworthiness of IoT devices in real time. By extending an established analytical trust model, we refine existing constructs and introduce new dimensions such as context awareness and behavioral monitoring to account for the operational variability of edge devices. Our proposed model computes risk and trust scores using weighted metrics, driving automated decisions and mitigation actions via a closed feedback loop. FedTrust provides a scalable and resilient approach for securing federated IoT ecosystems through continuous, data-driven trust calibration.

Ensuring the confidentiality of transmitting sensitive image data is paramount. Cryptography recreates a critical function in safeguarding information from potential risks and confirming the identity of authorised individuals, thereby addressing the growing demand for enhanced image security. This paper presents a novel AES-permuted Feistel Blowfish (APFB) model that aims to improve image data security cost-effectively and enhance data protection by incorporating AES and Blowfish algorithms. The proposed model's utility over existing approaches stems from its computational efficiency and speed. The proposed model's resilience and security against several attack modalities are validated through a comprehensive range of methods, including extensive experimentation, histogram analysis, PSNR, entropy, MSE, CC, computational time, and NIST statistical tests. The outcomes yielded a PSNR of 67.26, NPCR of 99.6354, and UACI of 33.412. Additionally, the applicability of the proposed model is validated by utilising a practical case analysis. The outcomes exhibit the relevance of the proposed model in real-world applications.

Both intentional attacks and accidental technical failures can lead to abnormal behaviour in components of industrial control systems. In our previous work, we developed a framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two classes, including knowledge elicitation to construct the directed acyclic graph of BN models. In this paper, we add a systematic method for knowledge elicitation to construct the Conditional Probability Tables (CPTs) of BN models, thereby completing a holistic framework to distinguish between attacks and technical failures. In order to elicit reliable probabilities from experts, we need to reduce the workload of experts in probability elicitation by reducing the number of conditional probabilities to elicit and facilitating individual probability entry. We utilise DeMorgan models to reduce the number of conditional probabilities to elicit as they are suitable for modelling opposing influences i.e., combinations of influences that promote and inhibit the child event. To facilitate individual probability entry, we use probability scales with numerical and verbal anchors. We demonstrate the proposed approach using an example from the water management domain.

Abstract Water management infrastructures such as floodgates are critical and increasingly operated by Industrial Control Systems (ICS). These systems are becoming more connected to the internet, either directly or through the corporate networks. This makes them vulnerable to cyber-attacks. Abnormal behaviour in floodgates operated by ICS could be caused by both (intentional) attacks and (accidental) technical failures. When operators notice abnormal behaviour, they should be able to distinguish between those two causes to take appropriate measures, because for example replacing a sensor in case of intentional incorrect sensor measurements would be ineffective and would not block corresponding the attack vector. In the previous work, we developed the attack-failure distinguisher framework for constructing Bayesian Network (BN) models to enable operators to distinguish between those two causes, including the knowledge elicitation method to construct the directed acyclic graph and conditional probability tables of BN models. As a full case study of the attack-failure distinguisher framework, this paper presents a BN model constructed to distinguish between attacks and technical failures for the problem of incorrect sensor measurements in floodgates, addressing the problem of floodgate operators. We utilised experts who associate themselves with the safety and/or security community to construct the BN model and validate the qualitative part of constructed BN model. The constructed BN model is usable in water management infrastructures to distinguish between intentional attacks and accidental technical failures in case of incorrect sensor measurements. This could help to decide on appropriate response strategies and avoid further complications in case of incorrect sensor measurements.