Trusted Computing Research Articles

TIP: A trust inference and propagation model in multi-human multi-robot teams

Trust is a crucial factor for effective human–robot teaming. Existing literature on trust modeling predominantly focuses on dyadic human-autonomy teams where one human agent interacts with one robot. There is little, if not no, research on trust modeling in teams consisting of multiple human and robotic agents. To fill this important research gap, we present the Trust Inference and Propagation (TIP) model to model and estimate human trust in multi-human multi-robot teams. In a multi-human multi-robot team, we postulate that there exist two types of experiences that a human agent has with a robot: direct and indirect experiences. The TIP model presents a novel mathematical framework that explicitly accounts for both types of experiences. To evaluate the model, we conducted a human-subject experiment with 15 pairs of participants (N=30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N=30$$\\end{document}). Each pair performed a search and detection task with two drones. Results show that our TIP model successfully captured the underlying trust dynamics and significantly outperformed a baseline model. To the best of our knowledge, the TIP model is the first mathematical framework for computational trust modeling in multi-human multi-robot teams.

PPAT: An effective scheme ensuring privacy-preserving, accuracy, and trust for worker selection in mobile crowdsensing networks

The data content privacy protection and data accuracy are two important research issues in Mobile Crowdsensing (MCS). However, current researches have rarely been able to satisfy privacy protection as well as data accuracy at the same time, thus hindering the development of MCS. To solve the above issues, for the first time, we have proposed a Privacy Preserving, Accuracy, and Trust data collection scheme (PPAT) for MCS, which can protect the privacy of data content and maintain high accuracy at low-cost style. In PPAT scheme, First, we proposed a scrambled data privacy protection framework which can protect the data of each worker from being known to any third party, which can protect the data privacy of workers. The second, more importantly, we propose a truth value estimation method based on trust computing, which can obtain the truth value more accurately compared to the classic methods under privacy-preserving. In the proposed trust-based truth value calculation, the worker's trust is determined by comparing it with the weight of the trusted worker. Then, the truth value is calculated by the trust of the workers, so that the truth value obtained is more accurate. Through theoretical analysis, it is proved that the proposed PPAT scheme has good worker data content, worker trust, and truth value content privacy protection. Through a large number of simulation experiments, the strategy proposed in this paper has a good ability to protect data content privacy compared to the previous strategy, while improving data quality by 0.5%∼5.7%, and reducing data collection costs by 35.6%∼54.9%.

Trust Computation Model for IoT devices using Soft Computing and Machine Learning Techniques for Health Care Applications

The calculation of trust for online web services is of utmost importance due to the security risks associated with network devices. various cloud platforms offer diverse services for communicating with various IoT devices and generating predictions based on the collected data. Occasionally, data may be compromised by either internal or external groups through low-impact hacking techniques. Investigating assaults and ensuring the elimination of connections can be a laborious task in a sensitive setting. Despite some progress, there are still many unresolved security issues that provide barriers to addressing security problems. This article seeks to conduct a comprehensive examination of existing security solutions for the lowest layer of the Internet of Things (IoT) and promote the development of more inventive IoT designs for those who invest in them. This paper proposes a system for calculating trust in IoT devices using both supervised and unsupervised machine learning algorithms for non-invasive health care applications. Both strategies were assessed based on trust computation weights and compared with their respective machine learning algorithms. The K-means clustering technique is employed for one strategy, while the SVM algorithm is used for the other approach. The aim of this successful implementation is to identify and eradicate a minor threat that occurs during the trusted execution process. An experimental analysis was conducted using NS2 2.35 in combination with an open-source Java platform. At first, a total of 100 nodes are created in the simulation environment. These nodes are then assessed using a clustering method to determine if they are trustworthy or untrustworthy, resulting in the generation of labels. The experimental research demonstrated the superior efficacy of attack detection in comparison to traditional methods of attack detection.

A Two-Phase Blockchain-Enabled Framework for Securing Internet of Medical Things Systems

The healthcare industry has witnessed a transformative impact due to recent advancements in sensing technology, coupled with the Internet of Medical Things (IoMTs)-based healthcare systems. Remote monitoring and informed decision-making have become possible by leveraging an integrated platform for efficient data analysis and processing, thereby optimizing data management in healthcare. However, this data is collected, processed, and transmitted across an interconnected network of devices, which introduces notable security risks and escalates the potential for vulnerabilities throughout the entire data processing pipeline. Traditional security approaches rely on computational complexity and face challenges in adequately securing sensitive healthcare data against evolving threats, thus necessitating robust solutions that ensure trust, enhance security, and maintain data confidentiality and integrity. To address these challenges, this paper introduces a two-phase framework that integrates blockchain technology with IoMT to enhance trust computation, resulting in a secure cluster that supports the quality-of-service (QoS) for sensitive data. The first phase utilizes the decentralized interplanetary file system and hashing functions to create a smart contract for device registration, establishing a resilient storage platform that encrypts data, improves fault tolerance, and facilitates data access. In the second phase, communication overhead is optimized by considering power levels, communication ranges, and computing capabilities alongside the smart contract. The smart contract evaluates the trust index and QoS of each node to facilitate device clustering based on processing capabilities. We implemented the proposed framework using OMNeT++ simulator and C++ programming language and evaluated against the cutting-edge IoMT security approaches in terms of attack detection, energy consumption, packet delivery ratio, throughput, and latency. The qualitative results demonstrated that the proposed framework enhanced attack detection by 6.00%, 18.00%, 20.00%, and 27.00%, reduced energy consumption by 6.91%, 8.19%, 12.07%, and 17.94%, improved packet delivery ratio by 3.00%, 6.00%, 9.00%, and 10.00%, increased throughput by 7.00%, 8.00%, 11.00%, and 13.00%, and decreased latency by 4.90%, 8.81%, 11.54%, and 20.63%, against state-of-the-art methods and was supported by statistical analysis.

CATcAFSMs: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems

<scp>CATcAFSMs</scp>: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems

Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms

Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms

Adaptive multidimensional trust-based recommendation model for peer to peer applications

Abstract In today’s world, the widespread utilization of services such as Nearby Share, Near Field Communication (NFC), and Wi-Fi Direct for deploying various applications has significantly bolstered the demand for reliable and secure distributed ad-hoc peer-to-peer networks. Yet, ensuring the trustworthiness of participating nodes remains a significant challenge. Trust among nodes plays a pivotal role in collaborative network applications, especially in environments like Mobile Ad-hoc Networks and VANET (Vehicular Ad-hoc Networks). Evaluating the trustworthiness of nodes is essential for promptly identifying misleading entities, thereby preemptively preventing their involvement in ongoing transactions. Attributes or characteristics exhibited by nodes, such as honesty, selfishness, or malicious behavior, serve as key factors in trust computation. The effectiveness of trust evaluation directly influences the encouragement of honest nodes and the deterrence of malicious ones, thereby nurturing a healthy and competitive network ecosystem. Recognizing the dynamic nature of network environments, trust computation methods must be adaptable and diverse. The adaptive multidimensional trust (AMT) model introduced in this article goes beyond simple reputation assessment. It offers three distinct methods such as Direct Trust ( Direct Trust ) \left({{\rm{Direct}}}_{{\rm{Trust}}}) , multiple security parameters, identification of qualified recommenders, which got selected dynamically as per change in trust ratings of peers. AMT advocates for an incentive-driven approach to identify legitimate peers, monitoring gradual increases in their performance ratings, whereas, spikes in performance alert to potential colluding peers or nodes displaying erratic behavior. This article evaluates the effectiveness of the AMT through a case study focused on an E-commerce application. It scrutinizes the model’s performance across different percentages of malicious nodes within the network, providing a thorough analysis and discussion of the results based on the trust value of malicious and benign peers and efficiency by selecting genuine service for transaction.

Developing a Manufacturing Industrial Brain in a Smart City: Analysis of fsQCA Based on Yiwu Knitting Industry Platform

Developing a Manufacturing Industrial Brain in a Smart City: Analysis of fsQCA Based on Yiwu Knitting Industry Platform

A comparison study of two implemented fuzzy-based models for decision of logical trust

In recent years, the human-to-human and human-to-things relationships are becoming complicated and unreliable, which makes harder decisions in a variety of situations. As a result, trust computing is gaining interest in a number of research fields. The Logical Trust (LT) is one of trust computing concepts. In this paper, we design a Fuzzy-based System for Decision of Logical Trust (FSDLT). We implement two models: FSDLTM1 and FSDLTM2. The FSDLTM1 considers three input parameters: Belief (Be), Experience (Ep), Rationality (Ra) and the output parameter is LT. In FSDLTM2, we consider Reliability (Re) as a new parameter. We evaluated the implemented models by computer simulations. The simulation results show that when Be, Ep, Ra and Re are increasing, the LT is increased. For FSDLTM1, when Ep value is 0.9, all LT values are greater than 0.5. While for FSDLTM2, in case when Be is 0.9, for all values of Ra and Re, when Ep is 0.5 and 0.9, all LT values are higher than 0.5. This shows that the person or device is trustworthy. The FSDLTM2 is more complex than FSALTM1 but it makes a better decision for LT by considering four input parameters.

Cyber security analysis of connected vehicles

AbstractThe sensor‐enabled in‐vehicle communication and infrastructure‐centric vehicle‐to‐everything (V2X) communications have significantly contributed to the spark in the amount of data exchange in the connected and autonomous vehicles (CAV) environment. The growing vehicular communications pose a potential cyber security risk considering online vehicle hijacking. Therefore, there is a critical need to prioritize the cyber security issues in the CAV research theme. In this context, this paper presents a cyber security analysis of connected vehicle traffic environments (CyACV). Specifically, potential cyber security attacks in CAV are critically investigated and validated via experimental data sets. Trust in V2X communication for connected vehicles is explored in detail focusing on trust computation and trust management approaches and related challenges. A wide range of trust‐based cyber security solutions for CAV have been critically investigated considering their strengths and weaknesses. Open research directions have been highlighted as potential new research themes in CAV cyber security area.

Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things

Trustworthiness possesses numerous design challenges for the researchers and scientists in the global era of internet of things. The solution to deal with these challenges lies in trust management mechanisms (TMM). The integration of TMM on the Internet of things (IoT) adds a higher level of security and reliability for the resource utilization and management. On the other hand, the efficient and optimal implementation of TMM in IoT network requires significant efforts and innovations. In this paper, an innovative edge based trustworthy framework (EBTF) using novel cooperative centrality-based system (NCCBS) has been proposed and implemented for IoT networks. Trustworthiness has been computed using factors like direct trust, recommendation, global trust, and centralities. The NCCBS has been compared with existing mechanism namely: Distributive Reputation Management (DRM), Personalized Similarity Measure (PSM), and Reliable Trust Computation Mechanism (RTCM). The NCCBS outperformed the existing approaches with the adoption of dynamic weight computation scheme. The performance of IoT networks has been evaluated on parameters like recommendation weight, trust index, interaction success rate and malicious behaviour analysis. Simulations have been carried out to evaluate and validate the proposed NCCBS and it depicts optimal performance with reference to the existing schemes.

Malicious Social Bot Using Twitter Network Analysis in Django

Malicious social bots generate fake tweets and automate their social relationships either by pretending to be a followers or by creating multiple fake accounts with malicious activities. Moreover, malicious social bots post shortened malicious URLs in the tweets to redirect the requests of online social networking participants to some malicious servers. Hence, distinguishing malicious social bots from legitimate users is one of the most important tasks in the Twitter network. To detect malicious social bots, extracting URL-based features (such as URL redirection, frequency of shared URLs, and spam content in URL) consumes less amount of time in comparison with social graph-based features (which rely on the social interactions of users). Furthermore, malicious social bots cannot easily manipulate URL redirection chains. In this article, learning automata-based malicious social bot detection (LA-MSBD) algorithm is proposed by integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. The proposed trust computation model contains two parameters, namely, direct trust and indirect trust. Moreover, the direct trust is derived from Bayes’ theorem, and the indirect trust is derived from the Dempster– Shafer theory (DST) to determine the trustworthiness of each participant accurately. Finally, we shown the user tweet data in terms of graph visualization of bar chart and pie chart of the system. Experimental results shown the better performance of the system.

A trustworthy data collection scheme based on active spot-checking in UAV-Assisted WSNs

The UAV-WSN systems integrates the flexibility of UAVs with the communication and sensing capabilities of WSNs, making them important means of information monitoring, collection, and analysis in various fields that has received widespread attention. However, various malicious attacks pose threats to the security of UAV-WSN systems, resulting in serious vulnerabilities in trustworthy data collection. Therefore, in this article, we propose an Active Spot-Checking-based Trustworthy Data Collection (ASC-DC) scheme, which makes the following main contributions: (1) A novel UAV-assisted active and verifiable node trust evaluation framework is proposed. It includes an active trust acquisition mechanism based on spot-checked factors of nodes and a node trustworthy interaction mechanism based on Authoritative Information Tables (AIT). Through actively spot-check nodes using UAV, this framework can rapidly and accurately evaluate node trustworthiness while assisting sensor nodes in establishing trust relationships quickly. (2) A layered trust computation model is proposed, where the primary trust computation and node security supervision tasks are offloaded to more powerful UAV, thereby reducing information congestion and node energy consumption caused by trust evaluation in the WSN. (3) An improved Ant Colony Optimization (ACO) algorithm is proposed to efficiently acquire UAV’s spot-checking flight trajectories. It reduces the cost of spot-checking nodes using UAV and improves the efficiency of trust verification. Experimental results demonstrate that the proposed ASC-DC scheme can accurately evaluate node trustworthiness in an efficient way, ensuring reliable data collection in UAV-WSN systems.

Potential use of semantic blockchain technology in smart construction

In recent years, the increased attention in distributed ledger technologies (DLTs) has sparked interest in their potential applications across various industries. This paper delves into the realm of the construction industry and explores the advancements made in semantic blockchain technology and its potential implications for smart construction practices. The primary research objective is to review the suitability of semantic blockchain approaches in specific usage domains within the construction sector. These domains include contracting with stakeholders, financial contracting, design and monitoring. To achieve this objective, the study investigates the integration of digital identity, access-control management, reputation management, trust computing and semantic blockchain approaches into construction processes. Through this analysis, the paper identifies the potential benefits in terms of enhanced efficiency, improved collaboration, heightened security and more informed decision making. Ultimately, these advancements are poised to create positive outcomes for all stakeholders involved in the construction process.

A Blockchain-Based Trustworthy Access Control Scheme for Medical Data Sharing

Blockchain is commonly employed in access control to provide safe medical data exchange because of the characteristics of decentralization, nontamperability, and traceability. Patients share personal health data by granting access rights to users or medical institutions. The major purpose of the existing access control techniques is to identify users who are permitted to access medical data. They hardly ever recognize internal assailants from legitimate entities. Medical data will involve multilayer access within the authorized organizations. Considering the cost of permissions management and the problem of insider malicious node attacks, users hope to implement authorization constraints within the authorized institutions. It can prevent their data from being maliciously disclosed by end-users from different authorized healthcare domains. For the purpose to achieve the fine-grained permissions propagation control of medical data in sharing institutions, a trust-based authorization access control mechanism is suggested in this study. Trust thresholds are assigned to different privileges based on their sensitivity and used to generate zero-knowledge proof to be broadcasted among blockchain nodes. This method evaluates the trust of each user through the dynamic trust calculation model. And meanwhile, smart contract is employed to verify whether the user’s trust can activate some permissions and ensure the privacy of the user’s trust in the process of authorization verification. In addition, the authorization transaction between users and institutions is recorded on the blockchain for patient traceability and accountability. The feasibility and effectiveness of the scheme are demonstrated through comprehensive comparisons and extensive experiments.

Trust Aware Nero Fuzzy Based Agglomerative Hierarchical Clustering with Secure Whale Optimization Routing for Enhancing Energy Efficiency in WSN

Wireless sensor networks (WSNs) comprise a network of dispersed, carefully positioned sensor nodes in their deployment environment to monitor and collect data on natural phenomena. These sensor nodes collaborate to transmit data via multi-hop communication, ultimately reaching a central base station for processing. However, WSNs face significant challenges due to the resource-constrained nature of these devices and the harsh, open environments in which they operate. Addressing energy optimization and ensuring secure communication are primary concerns in the successful operation of WSNs. This paper introduces anovelTrust aware Neuro Fuzzy Clustering head selection (TNFCH) and agglomerative hierarchical clustering approach (AHC) with Secure Whale Optimization (SWO) Algorithm Routing to enhance energy-efficient transmission in WSNs. Our proposed protocol (TNFCH-AHWO) efficiently organizes nodes by utilizing neural network and Fuzzy logic then securely transfers the data into the communication network. We employ a Trust calculation algorithm in our system to ensure Trust and data integrity, facilitating efficient lightweight operations such as key generation, encryption, decryption, and verification. This ensures hop-to-hop authentication among the nodes in WSNs. To assess the performance of our proposed protocol, we conducted simulations using the NS3 simulator. The findings of the simulation show that the suggested protocol greatly enhances various performance metrics, including energy consumption analysis, throughput, network delay, network lifetime, and packet delivery ratio when compared to existing protocols.

SocIoTy: Practical Cryptography in Smart Home Contexts

Smartphones form an important source of trust in modern computing. But, while their mobility is convenient, smartphones can be stolen or seized, allowing an adversary to impersonate the user in their digital life: accessing the user's services and decrypting their sensitive files. With this in mind, we build SocIoTy, which leverages a user's existing IoT devices to add a context-sensitive layer of security for non-expert users. Instead of assuming the existence of dedicated hardware, SocIoTy re-uses the devices of a user's smart home to provide cryptographic services, which we term at-home cryptography. We show that at-home cryptography can be built from simple cryptographic primitives, and that our SocIoTy solution is able to provide useful functionalities, like two-factor authentication (2FA) and secure file storage, while protecting against powerful adversaries in this setting. We implement and evaluate SocIoTy in real-world use cases and provide microbenchmarks for individual cryptographic operations on realistic models of IoT devices. We also provide full benchmarks of an end-to-end deployment on a simulated smart home, using a smartphone and 9 IoT devices to generate and display 2FA one-time passwords in less than 200 milliseconds. SocIoTy is able to provide strong, practical cryptography while binding its execution to the smart home itself, all without requiring additional hardware.

Security threats and mitigation techniques affecting trust computation in cloud computing

Security threats and mitigation techniques affecting trust computation in cloud computing

BTC2PA: A Blockchain-Assisted Trust Computation With Conditional Privacy-Preserving Authentication for Connected Vehicles

BTC2PA: A Blockchain-Assisted Trust Computation With Conditional Privacy-Preserving Authentication for Connected Vehicles

알고리즘 기반 미디어 수용에 관한 연구

The goal of this study is to look into how and what effects algorithmically designed media have in a certain setting. Specifically, we did focus group interviews with people who use ADS media. The goal was to find out how they used ADS media, figure out how willing they were to accept algorithms based on those behaviors, and talk about literacy programs and ways to keep people from accepting optimal ADS media. For a quick summary, here are our results. First, people who used ADS media mostly thought about the cost-benefit ratio, though there were differences between age groups. Also, younger people were more focused on computing accuracy, while older people were more focused on how things fit into the bigger picture. Aside from that, people were stuck in a state of computational trust, where they had mixed feelings about digital and traditional confidence. Furthermore, people didn't understand how much data was worth in money, so they didn't see how important it was to get paid for their work.