Trusted Computing Research Articles

A machine learning based approach for user privacy preservation in social networks

With the development of Internet technology, service providers can provide users with personalized services to enrich user experience, however, this often requires a large number of users’ private data. Meanwhile, the protection of their private data and the evaluation of the risk of leaked datasets become a matter of great concern to many people. To resolve these issues, in this paper, we develop a machine learning-based approach in online social networks (OSNs) to efficiently correlate the leaked datasets and accurately learn millions of users’ confidential information. Moreover, a trust evaluation model is developed in OSNs to identify malicious service providers and secure users’ social activities via direct trust computing and indirect trust computing. Extensive experiments are conducted by using real-world leaked datasets, and the results show that the efficiency and effectiveness of the proposed approach in terms of user privacy protection and accuracy of privacy leakage evaluation.

Fuzzy COmplex PRoportional ASsessment of alternatives‐based Node Cooperation Enforcing Trust Estimation Scheme for enhancing Quality of Service during reliable data dissemination in Mobile Ad hoc Networks

SummaryTrust is considered as the most significant factor that helps in enforcing cooperation among the autonomous nodes of Mobile Ad hoc Network (MANETs). The computation of trust is essential for assessing the level of cooperation rendered by each individual mobile toward the activity of data routing. The calculation of trust in a self‐configuring and infrastructure‐less network is a challenge due to the dynamic movement of mobile nodes. Moreover, the trust of intermediate nodes in the routing path needs to be estimated for preventing the deterioration of network performance. In this paper, a Fuzzy COPRAS‐based Node Cooperation Enforcing Trust Estimation (FCOPRAS‐NCETE) Scheme was proposed for maintaining superior Quality of Service (QoS) in the event of reliable data dissemination. This proposed FCOPRAS‐NCETE Scheme was proposed for comprehensive ranking of mobile nodes acting as intermediate nodes between the source and destination nodes. It incorporated the benefits of fuzzy set theory and COmplex PRoportional ASsessment of alternatives (COPRAS) to determine the trust of intermediate nodes for isolating them from the routing path in the uncertain routing process with the objective to maintain superior QoS in the network. The simulation experiments of the proposed FCOPRAS‐NCETE Scheme conducted using ns‐2 confirmed its predominance in improving the throughput, packet dissemination rate, packet loss rate, energy consumptions and total overhead with different number of mobile nodes, CBR traffic rate and pairs of source and destination nodes in the network.

Trust Management Approach for Detection of Malicious Devices in SIoT

Internet of Things (IoT) is an innovative era of interrelated devices to provide services to other devices or users. In Social Internet of Thing (SIoT), social networking aspect is used for building relationships between devices. For providing or utilizing services, devices need to trust each other in complex and heterogeneous environments. Separating benign and malicious devices in SIoT is a prime security objective. In literature, several works proposed trust computation models based on trust features. But these models fail to identify malicious devices. This paper focuses on detection of malicious devices. In this paper, basic fundamentals, properties, models and attacks of trust in SIoT are discussed. Up-to-date research distributions on trust management and trust attacks are reviewed and idea of Trust Management using Machine Learning Algorithm (TM-MLA) is proposed for identification of malicious devices.

Resource allocation and trust computing for blockchain-enabled edge computing system

In order to meet various needs of people, different Internet of Things (IoT) devices have been developed and applied successfully in recent years. However, the consequent challenges in terms of search efficiency, reliable requirements, and resource allocation appear followed, which attract attention from both academia and industry. Facing this circumstance, it is necessary to establish a new scheme to realize data processing and sharing better. Therefore, a reliable and efficient system based on edge computing and blockchain is proposed in this paper. First, a new group-agent strategy with trust computing is designed to ensure the reliability of edge devices during interactions and improve transmission efficiency. Second, we introduce a stacked task sorting and ranking mechanism which improves resource allocation in each edge device. Third, this paper creates a new content model that uses Zipf distribution to predict context popularity of keywords and encrypt hot data with symmetric searchable encryption (SSE) technology. Finally, simulation results show that the proposed scheme has better computational efficiency and higher reliability compared with existing methods.

RETRACTED: An approach to remove duplication records in healthcare dataset based on Mimic Deep Neural Network (MDNN) and Chaotic Whale Optimization (CWO)

Duplication of data in an application will become an expensive factor. These replication of data need to be checked and if it is needed it has to be removed from the dataset as it occupies huge volume of data in the storage space. The cloud is the main source of data storage and all organizations are already started to move their dataset into the cloud since it is cost effective, storage space, data security and data Privacy. In the healthcare sector, storing the duplicated records leads to wrong prediction. Also uploading same files by many users, data storage demand will be occurred. To address those issues, this paper proposes an Optimal Removal of Deduplication (ORD) in heart disease data using hybrid trust based neural network algorithm. In ORD scheme, the Chaotic Whale Optimization (CWO) algorithm is used for trust computation of data using multiple decision metrics. The computed trust values and the nature of the data’s are sequentially applied to the training process by the Mimic Deep Neural Network (MDNN). It classify the data is a duplicate or not. Hence the duplicates files are identified and they were removed from the data storage. Finally, the simulation evaluates to examine the proposed MDNN based model and simulation results show the effectiveness of ORD scheme in terms of data duplication removal. From the simulation result it is found that the model’s accuracy, sensitivity and specificity was good.

Research on Energy Efficiency Application of Wireless Communication Energy Saving and Emission Reduction Based on Self-organizing Network Game Technology

Energy efficiency is a very important performance index in wireless ad hoc networks. So far, there is no universally accepted definition of the energy efficiency of self-organizing networks, and most of the conclusions drawn are asymptotic or qualitative, and its practicality is very limited. Aiming at this problem, a trust model based on game theory is proposed. The model introduces a time decay function to improve the accuracy and dynamic adaptability of trust evaluation, and introduces a node transaction density function in the recommendation trust calculation to calculate the recommendation performance of the recommended node. Reliability: In order to effectively encourage nodes to actively provide high-quality services and punish bad behaviour nodes, game theory is also introduced to analyse the trust value of nodes and decide whether to forward messages according to the analysis results. The simulation results show that the algorithm can adaptively deploy network nodes according to the distribution of task requirements, and quickly achieve the optimal layout.

LWTSM-IoT: Light Weight Trust Sensing Mechanism for Internet of Things

In Internet of Things (IoT), secure communication is a prime concern since the open internet source and vast heterogeneity offers several challenges to the network. To achieve an enhanced security, an effective trust evaluation model is required through which the abnormal nodes can be detected and isolated. Towards this objective we have proposed a Light Weight Trust Sensing (LWTS) mechanism for IoT routing. Several factors like Packet Forwarding Factor, Packet Consistency Factor and Packet Repetition Factor are employed to analyze the behaviour of IoT nodes. Along with these factors, the proposed model also checks for energy efficiency to achieve an improved network lifetime. Trust Calculation process is accomplished in two phases; they are direct and indirect fashion. Finally based on obtained total trust, each neighbour node are categorized as No Trust, Average Trust, Fair Trust and Good Trust and the node with good trust is selected as next-hop forwarding node. For the proposed approach extensive simulations are carried out and the performance is measured through Packet Delivery Ratio, Malicious Detection Rate and Average Energy Consumption. The obtained results prove the effectiveness when compared to existing approaches.

When voting, trust computer scientists

"The right of suffrage is a fundamental Article in Republican Constitutions" [6]

Energy Aware Hierarchal Data Aggregation and Trust Based Data Integrity Verification for WSN

Currently the demand of wireless sensor networks has gained huge attraction due to its wide range of applications. Generally, these nodes are equipped with limited power resource and deployed in harsh environment where replacing these resources is a tedious task. Due to these issues, minimizing the energy consumption is a prime task to prolong the network lifetime. To overcome the challenging issue of data aggregation we introduced a novel combined mechanism which performs clustering and trust computing process to improve the data aggregation. According to this scheme, we arrange the nodes as normal node, advanced node and super nodes based on their residual energy parameters. The proposed model uses hierarchal scheme where we present a new mechanism for optimal number of cluster formation and cluster head selection. After selecting the cluster head, we apply trust computation scheme which provides sensing trust, link trust and node trust. The node trust is computed as direct and indirect trust. This trust mechanism is used as hop-by-hop manner to maintain he data integrity. The experimental study shows that proposed approach achieves better performance and maintains the security aspects of WSN.

AI based Computational Trust Model for Intelligent Virtual Assistant

The Intelligent virtual assistant (IVA) also called AI assistant or digital assistant is software developed as a product by organizations like Google, Apple, Microsoft and Amazon. Virtual assistant based on Artificial Intelligence which works and processes on natural language commands given by humans. It helps the user to work more efficiently and also saves time. It is human friendly as it works on natural language commands given by humans. Voice-controlled Intelligent Virtual Assistants (IVAs) have seen gigantic development as of late on cell phones and as independent gadgets in individuals’ homes. The intelligent virtual assistant is very useful for illiterate and visually impaired people around the world. While research has analyzed the expected advantages and downsides of these gadgets for IVA clients, barely any investigations have exactly assessed the need of security and trust as a singular choice to use IVAs. In this proposed work, different IPA users and non-users (N=1000) are surveyed to understand and analyze the barriers and motivations to adopting IPAs and how users are concerned about data privacy and trust with respect to organizational compliances and social contract related to IPA data and how these concerns have affected the acceptance and use of IPAs. We have used Naïve Byes Classifier to compute trust in IVA devices and further evaluate probability of using different trusted IVA devices.

A blockchain-based trust management method for Internet of Things

In Internet of things (IoT), one of the main concepts is wireless sensor networks where there are a large of resource-constrained sensor nodes. With the wide application of Internet of things (IoT), these resource-constrained sensor nodes have to suffer from more security threats. One of possible solutions is to establish a trustworthy IoT environment where the interactions are based on the trustworthiness of sensor nodes. More and more scholars focus on studying trust management and trust evaluation in IoT, but there is few research in ensuring that the feedbacks used in the trust evaluating process is trustworthy. Furthermore, a resource-constrained IoT node does not have enough memory to satisfy the usage of trust computation and maintenance. To solve the problem, this paper proposes a blockchain-based trust management mechanism (BBTM), where the trustworthiness of sensor nodes will be evaluated by the mobile edge nodes. BBTM can create smart contracts for trust computation and verify the computation process. We analyze BBTM performance in terms of trust accuracy, convergence, and resiliency against attacks. In addition, we also compare BBTM with other methods. Experimental results demonstrate the effectiveness of the proposed method.

Construction of Trusted Routing Based on Trust Computation

In the field of applied IoT, a large number of wireless sensor devices are tasked with data production and collection, providing IoT subjects with a large amount of basic data to support top‐level IoT applications. However, there is a considerable risk of being attacked on such sensor networks that are organized in a wireless form. These relatively independent network devices have extremely limited performance and lifetime, a problem that can be supplemented in a centralized network with base stations by relying on the performance of the core nodes of the network, but in a decentralized self‐organizing network, they can have a serious adverse impact on the implementation of security solutions. Considering the fundamental nature of the data generated by such end devices in IoT application services, the protection of their security is also directly related to the quality of upper layer services provided. The main research result of this paper is the design of a trust routing scheme for self‐organizing networks. The scheme is based on a comprehensive evaluation of data transmission rate, transmission delay, and other factors related to the operation status of the self‐organized network and improves the efficiency of the overall work of the self‐organized network by reducing the performance consumption of individual nodes of the self‐organized network and balancing the network load.

Trust computation in web services based on Beth's trust model

Trust computation in web services based on Beth's trust model

Trust computation in web services based on Beth's trust model

Security mechanism is obviously vital in any systems especially when it comes to web services. To protect web resources from malicious attacks, trust relation is required between entities. Trust relation between entities was a key factor for the successful rate of web operation. Against the problem of failing to punish malicious recommendation, Beth's trust model was improved by introducing a punishment factor; it makes sure that the transaction behaviours can be reflected by trust degree so as to eliminate malicious recommendations. In this way the trust model is more reasonable and the trust value is more subjective. Experiment results show that the trust value of an entity increased according to more successful transactions, which embodies the purpose of giving high-trust-degree entity priority to access services and punishing malicious entities. When malicious entity's proportion is large, a higher weight factor φ can guarantee successful transaction rate via better recognising malicious entities.

Shielding against on-off attack pattern in a MANET through a trust computation scheme leveraging the behavioral consistency

The evolution of security mechanisms for defending a mobile ad hoc network (MANET) is challenged through intelligent strategies employed by the attackers. The current research involves the design of a robust trust management framework (TMF) which protects the network against an attack pattern termed as on-off attack. Each dimension of the attack vector corresponding to packet droppers and dishonest recommenders employing the on-off attack pattern is countered through an individual dimension of the multi-dimensional trust vector termed as forwarding consistency trust and recommendation consistency trust respectively. The efficiency of proposed scheme in reducing the packet loss is analysed through network simulation using NS-2 under different attack scenarios. It is found that incorporation of behavioural consistency in the trust computation plays a very significant role in maintaining the network performance in the presence of on-off attackers by improving the packet delivery fraction from 71% to 85% and the detection ratio from 70% to 95% respectively.

Shielding against on-off attack pattern in a MANET through a trust computation scheme leveraging the behavioural consistency

Shielding against on-off attack pattern in a MANET through a trust computation scheme leveraging the behavioural consistency

ETAS: An Efficient Trust Assessment Scheme for BANs

A wireless body area network (WBAN) is a wireless network of wearable computing devices and intelligent physiological sensors. The intelligent physiological sensors collect and process sensitive data from the patient body. The security, reliability, and trustworthiness of sensitive data collected and processed by intelligent physiological sensors are critical due to its unique application domain. Moreover, consistent and reliable data gathering along with its transmission also plays a vital role in WBANs. Trust management (in BANs) has been found as a useful tool to improve cooperation among sensor nodes, security as well as reliability. The paper recommends a novel and efficient, lightweight trust assessment scheme (ETAS) suitable for health application domains and does not rely purely on any encryption technique. The primary purpose is to develop an exciting comprehensive, novel trust estimation framework for BANs to enhance reliability, dependability, security by isolating compromised (hotspot) nodes with great resource efficiency. ETAS incorporates several exclusive (unique) features like efficient trust evaluator, secure and attack resistant, along with competent trust aggregator function to achieve comprehensive trust score. The trust evaluator function is a multi-trust (communication trust, data trust, and energy trust) strategy to deal with severe internal security threats such as badmouthing attack, ballot-stuffing attack, sybil attack, traitor attack, etc. with less resource consumption. Moreover, ETAS incorporates both the success rate and misbehavior component during trust evaluation. The success rate record the number of successful/unsuccessful interactions among sensor nodes in terms of packet send/receive. The misbehavior component keeps records of current and past misbehavior of sensor nodes for effective decision making. Moreover, ETAS focus on the frequency of interaction among biomedical sensor nodes within a specified period to analyze their behavior for efficient trust decisions. Furthermore, ETAS incorporates temperature, data trust as well as the trust score of biomedical sensors to identify hotspot nodes in BSN. ETAS offers full flexibility to adjust the trust threshold, trust domain, reward, and punishment term according to system and application requirements. ETAS's efficiency is validated through several outcomes (MATLAB R2019a) along with theoretical analysis in terms of energy consumption, attack detection, mitigation, trust computation cost, and packet delivery ratio.

A Reliable Trust Computing Mechanism in Fog Computing

Due to the lack of trust on IoT devices, the integration of fog computing and IoT devices is hindered. Trust is considered to have two notions: subjective trust where the user puts his individual interests to the interactions and objective trust which depends only on individual interaction experiences. This paper proposes a reliable trust computing mechanism based on subjective and objective trust. The subjective trust is calculated from feedback of multiple sources. The incentive and punishment mechanism is applied to the subjective trust to avoid malicious devices. The objective trust is calculated based on quality of services. The overall trust helps the IoT devices to determine the trustworthiness of other IoT devices and in turn helps to establish a trusted environment. The experimental results show that the performance is better than existing methods in terms of time required to calculate the overall trust, reliability, and trustworthiness of IoT devices.

Trust Management-Based Service Recovery and Attack Prevention in MANET

The mobile ad-hoc network (MANET) output is critically impaired by the versatility and resource constraint of nodes. Node mobility affects connection reliability, and node resource constraints can lead to congestion, which makes the design of a routing MANET protocol with quality of service (QoS) very difficult. An adaptive clustering reputation model (ACRM) method is proposed to improve energy efficiency with a cluster-based framework. The proposed framework is employed to overcome the problems of data protection, privacy, and policy. The proposed ACRM-MRT approach that includes direct and indirect node trust computation is introduced along with the master recovery timer (MRT) for achieving an efficient service recovery process, and its service recovery time is calculated through the service execution process. During data transmission in MANET, various types of attacks can occur, of which the Sybil attack is the most dangerous. To address this problem, this paper proposed a method for the detection and prevention of Sybil attacks using a resilient scheme. The proposed method can improve system energy efficiency and address security, safety, and privacy issues of wireless network applications. Finally, the performance of the proposed method is evaluated regarding the time delay, throughput, energy efficiency, control overhead, and detection rate. The simulation results show that the proposed ACRM-MRT method can effectively improve the time delay, throughput, energy efficiency, control overhead, and detection rate compared to the existing methods. Topological change adaptive ad-hoc on-demand multi-path distance vector (TA-AOMDV)and ad-hoc on-demand multi-path distance vector (AOMDV) are simulated on the NS2 platform for the data rate in the range of 4–40 kbps and the number of nodes in the range of 10-100. The proposed method can reduce the service recovery time in the case of faults during service execution and can be used in real-time applications traffics since it is mostly affected by failure through the occurrence of delay and loss of packets.

Decision Making in Internet of Vehicles Using Pervasive Trusted Computing Scheme

Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention. Recently Internet of Vehicles (IoVs) has been introduced as one of the applications of pervasive computing that addresses the road safety challenges. Vehicles participating within the IoV are embedded with a wide range of sensors which operate in a real time environment to improve the road safety issues. Various mechanisms have been proposed which allow automatic actions based on uncertainty of sensory and managed data. Due to the lack of existing transportation integration schemes, IoV has not been completely explored by business organizations. In order to tackle this problem, we have proposed a novel trusted mechanism in IoV during communication, sensing, and record storing. Our proposed method uses trust based analysis and subjective logic functions with the aim of creating a trust environment for vehicles to communicate. In addition, the subjective logic function is integrated with multi-attribute SAW scheme to improve the decision metrics of authenticating nodes. The trust analysis depends on a variety of metrics to ensure an accurate identification of legitimate vehicles embedded with IoT devices ecosystem. The proposed scheme is determined and verified rigorously through various IoT devices and decision making metrics against a baseline solution. The simulation results show that the proposed scheme leads to 88% improvement in terms of better identification of legitimate nodes, road accidents and message alteration records during data transmission among vehicles as compared to the baseline approach.