Trustworthy Peers Research Articles

Dynamic Defense Model against Eclipse Attacks in Proof-of-Work Blockchain Systems

An emerging threat within blockchain systems is the malicious tactic known as eclipse attacks, wherein attackers aim to deceive and gain control over targeted nodes. Despite the increasing popularity of blockchain technology, effective defence mechanisms against eclipse attacks have been lacking. A novel and dynamic defence model designed explicitly for Proof-of-Work (PoW) consensus blockchain systems is proposed to address this critical issue. The model relies on a node-to-node evaluation mechanism to ensure robust protection. By leveraging mutual evaluations conducted between clients and incorporating the renowned Kademlia algorithm, a ranking value is assigned to each node within the defence model. These rankings empower nodes to prudently select trustworthy peers as neighbours based on a threshold comparison, effectively mitigating the risk posed by malicious nodes. Notably, the proposed approach significantly enhances the security of blockchain systems without necessitating modifications to existing protocols or network structures. The effectiveness of the proposed defence model in countering eclipse attacks is extensively validated through a series of experiments. Furthermore, the approach surpasses previous defence strategies, offering a higher probability of successful defence while maintaining minimal overhead. With its straightforward implementation and operational efficiency, the dynamic defence model is an ideal solution for real-world deployment scenarios. This research presents a compelling and robust defence model against eclipse attacks in PoW blockchain systems, addressing the existing gap in defence mechanisms. Furthermore, this advancement paves the way for the wider adoption of blockchain applications across various domains by providing enhanced security, reliability, and trustworthiness.

Challenges faced in the peer review system in open access journals

Challenges faced in the peer review system in open access journals

Incorporating AI and learning analytics to build trustworthy peer assessment systems

AbstractPeer assessment has been recognised as a sustainable and scalable assessment method that promotes higher‐order learning and provides students with fast and detailed feedback on their work. Despite these benefits, some common concerns and criticisms are associated with the use of peer assessments (eg, scarcity of high‐quality feedback from peer student‐assessors and lack of accuracy in assigning a grade to the assessee) that raise questions about their trustworthiness. Consequently, many instructors and educational institutions have been anxious about incorporating peer assessment into their teaching. This paper aims to contribute to the growing literature on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn can increase their trustworthiness and adoption. In particular, we present and evaluate our AI‐assisted and analytical approaches that aim to (1) offer guidelines and assistance to student‐assessors during individual reviews to provide better feedback, (2) integrate probabilistic and text analysis inference models to improve the accuracy of the assigned grades, (3) develop feedback on reviews strategies that enable peer assessors to review the work of each other, and (4) employ a spot‐checking mechanism to assist instructors in optimally overseeing the peer assessment process. Practitioner notesWhat is already known about this topic Engaging students in peer assessment has been demonstrated to have various benefits. However, there are some common concerns associated with employing peer assessment that raise questions about their trustworthiness as an assessment item. What this paper adds Methods and processes on how AI and learning analytics may be incorporated to address some of the common concerns associated with peer assessment systems, which in turn, can increase their trustworthiness and adoption. Implications for practice Presentation of a systematic approach for development, deployment and evaluation of AI and analytics approaches in peer assessment systems.

A Secure and User Privacy-Preserving Searching Protocol for Peer-to-Peer Networks

File sharing peer-to-peer networks have become quite popular of late as a new paradigm for information exchange among large number of users in the Internet. However, these networks suffer from several problems such as fake content distribution, free riding, whitewashing, poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. In this paper, a secure and efficient searching scheme for peer-to-peer networks has been proposed that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures among the trustworthy peers, the scheme provides a highly reliable module for protecting the privacy of the users and data in the network. Simulation results have demonstrated that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times.

AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks.

In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.

Design and evaluation of a novel hierarchical trust assessment approach for vehicular networks

In vehicular networks, vehicles communicate with each other and transfer messages to enhance the safety of both commuters and vehicles. Trust management in these networks has become a key issue because of the selfish behavior that is frequently exhibited by some vehicles. Most of the existing trust management schemes for vehicular networks schemes have adopted cryptographic techniques, which require high computation time and high network resource utilization. We propose a Hierarchical Trust Management System (HTMS) that relies on the trust metric computation for every individual vehicle. We also propose a systematic approach based on trust evaluation, trust propagation and trust aggregation to compute the trustworthiness of vehicles. Initially, the trust evaluation scheme computes a local trust score based on a behavioral analysis. Next, the proposed trust propagation model shares the computed local trust score to neighboring vehicles using a platoon-based dissemination technique. Based on the local trust score and propagated trust opinions, our aggregation technique computes a Global Trust score (GTS) using Dempster-Shafer theory. Finally, based on the computed GTS score, the proposed system invokes an action module to reward or punish vehicles. The simulation results obtained show that the proposed system is highly resilient to trust-based attacks and enables trustworthy peers to generate a higher number of data transfers within the network.

Effective Secrecy Rate for a Downlink NOMA Network

In this paper, a novel approach is introduced to study the achievable delay-guaranteed secrecy rate, by introducing the concept of the effective secrecy rate (ESR). This study focuses on the downlink of a non-orthogonal multiple access (NOMA) network with one base station, multiple single-antenna NOMA users and an eavesdropper. Two possible eavesdropping scenarios are considered: 1) an internal, unknown, eavesdropper in a purely antagonistic network; and 2) an external eavesdropper in a network with trustworthy peers. For a purely antagonistic network with an internal eavesdropper, the only receiver with a guaranteed positive ESR is the one with the highest channel gain. A closed-form expression is obtained for the ESR at high signal-to-noise ratio (SNR) values, showing that the strongest user’s ESR in the high SNR regime approaches a constant value irrespective of the power coefficients. Furthermore, it is shown the strongest user can achieve higher ESR if it has a distinctive advantage in terms of channel gain with respect to the second strongest user. For a trustworthy NOMA network with an external eavesdropper, a lower bound and an upper bound on the ESR are proposed and investigated for an arbitrary legitimate user. For the lower bound, a closed-form expression is derived in the high SNR regime. For the upper bound, the analysis shows that if the external eavesdropper cannot attain any channel state information (CSI), the legitimate NOMA user at high SNRs can always achieve positive ESR, and the value of it depends on the power coefficients. Simulation results numerically validate the accuracy of the derived closed-form expressions and verify the analytical results given in the theorems and lemmas.

Tensions in teacher choice and professional development

In a recent qualitative coaching study of a single district, Evthokia Stephanie Saclarides and Sarah Theule Lubienski observed tensions within a coaching structure in which teachers had to be the ones to initiate professional development with a coach. Specifically, the Teacher’s Choice structure heightened coaches’ difficulties gaining access to teachers’ classrooms, which led the coaches to position themselves as trustworthy peers and avoid difficult conversations. The authors recommend that districts adopt a blended approach where teachers set their own learning agendas but coaches can also initiate professional development with teachers.

TrustyFeer: A Subjective Logic Trust Model for Smart City Peer‐to‐Peer Federated Clouds

Cloud computing plays a major role in smart cities development by facilitating the delivery of various services in an efficient and effective manner. In a Peer‐to‐Peer (P2P) federated clouds ecosystem, multiple Cloud Service Providers (CSPs) collaborate and share services among them when experiencing a shortage in certain resources. Hence, incoming service requests to this specific resource can be delegated to other members. Nevertheless, the lack of preexisting trust relationship among CSPs in this distributed environment can affect the quality of service (QoS). Therefore, a trust management system is required to assist trustworthy peers in seeking reliable communication partners. We address this challenge by proposing TrustyFeer, a trust management system that allows peers to evaluate the trustworthiness of other peers based on subjective logic opinions, formulated using peers’ reputations and Service Level Agreements (SLAs). To demonstrate the utility of TrustyFeer, we evaluate the performance of our method against two long‐standing trust management systems. The simulation results show that TrustyFeer is more robust in decreasing the percentage of services that do not conform to SLAs and increasing the success rate of exchanged services by good CSPs conforming to SLAs. This should provide a trustworthy federated clouds ecosystem for a better, more sustainable future.

Do peer review models affect clinicians\u2019 trust in journals? A survey of junior doctors

BackgroundThe aim of this survey was to determine the level of awareness and understanding of peer review and peer review models amongst junior hospital doctors and whether this influences clinical decision-making.MethodsA 30-question online anonymous survey was developed aimed at determining awareness of peer review models and the purpose of peer review, perceived trustworthiness of different peer review models and the role of peer review in clinical decision-making. It was sent to 800 trainee doctors in medical specialties on the University College London Partners trainee database.ResultsThe response rate was (178/800) 22%. Most respondents were specialist registrars. Checking that research is conducted correctly (152/178, 85%) and the data interpreted correctly (148/178, 83%) were viewed as the most important purposes of peer review. Most respondents were aware of open (133/178, 75%), double-blind (125/178, 70%) and single-blind peer review (121/178, 68%). 101/178 (57%) had heard of collaborative, 87/178 (49%) of post publication and 29/178 (16%) of decoupled peer review. Of those who were aware of double-blind, single-blind open and collaborative peer review, 85 (68%), 82 (68%), 74 (56%) and 24 (24%), respectively, understood how they worked. The NEJM, Lancet and The BMJ were deemed to have most trustworthy peer review, 137/178 (77%), 129/178 (72%) and 115/178 (65%), respectively. That peer review had taken place was important for a journal content to be used for clinical decision-making 152/178 (85%), but the ability to see peer review reports was not as important 22/178 (12%). Most felt there was a need for peer review training and that this should be at the specialist registrar stage of training.ConclusionsJunior hospital doctors view peer review to be important as a means of quality control, but do not value the ability to scrutinize peer review themselves. The unquestioning acceptance of peer review as final validation in the field of medicine emphasises not only the responsibility held by medical journals to ensure peer review is done well but also the need to raise awareness amongst the medical community of the limitations of the current peer review process.

HonestPeer: An enhanced EigenTrust algorithm for reputation management in P2P systems

The visible success of the Peer to Peer (P2P) paradigm is associated with many challenges in finding trustworthy peers as reliable communication partners. Reputation management systems are emerging in the face of these challenges. The EigenTrust reputation management system is among the most known and successful reputation systems. On the other hand, a main drawback of this system is its reliance on a set of pre-trusted peers which causes nodes to center around them. As a consequence, other peers are ranked low despite being honest, marginalizing their effect in the system. To tackle this problem, this paper proposed enhancing the EigenTrust algorithm by giving peers with high reputation values (honest peers) a role in calculating the global reputation of other peers. Rather than solely depending on the static group of pre-trusted peers, the proposed algorithm, HonestPeer, selects the most reputable nodes, honest peers, dynamically based on the quality of the provided files. This makes HonestPeer more robust to the increase in the number of files and nodes in the system. Through simulation, it has been shown that HonestPeer has successfully maintained higher success rate and lower percentage of inauthentic downloads when compared to the original algorithm.

Long-term location privacy protection for location-based services in mobile cloud computing

The popularity of mobile devices, especially intelligent mobile phones, significantly prompt various location-based services (LBSs) in cloud systems. These services not only greatly facilitate people's daily lives, but also cause serious threats that users' location information may be misused or leaked by service providers. The dummy-based privacy protection techniques have significant advantages over others because they neither rely on trusted servers nor need adequate number of trustworthy peers. Existing dummy-based location privacy protection schemes, however, cannot yet provide long-term privacy protection. In this paper, we propose four principles for the dummy-based long-term location privacy protection (LT-LPP). Based on the principles, we propose a set of long-term consistent dummy generation algorithms for the LT-LPP. Our approach is built on soft computing techniques and can balance the preferred privacy protection and computing cost. Comprehensive experimental results demonstrate that our approach is effective to both long-term privacy protection and fake path generation for LBSs in mobile clouds.

Trustworthy-based efficient data broadcast model for P2P interaction in resource-constrained wireless environments

In a decentralised system like P2P where each individual peers are considerably autonomous, the notion of mutual trust between peers is critical. In addition, when the environment is subject to inherent resource constraints, any efficiency efforts are essentially needed. In light of these two issues, we propose a novel trustworthy-based efficient broadcast scheme in a resource-constrained P2P environment. The trustworthiness is associated with the peerʼs reputation. A peer holds a personalised view of reputation towards other peers in four categories namely SpEed, Correctness, qUality, and Risk-freE (SeCuRE). The value of each category constitutes a fraction of the reliability of individual peer. Another factor that contributes to the reliability of a peer is the peerʼs credibility concerning trustworthiness in providing recommendation about other peers. Our trust management scheme is applied in conjunction with our trust model in order to detect malicious and collaborative-based malicious peers. Knowledge of trustworthiness among peers is used in our proposed broadcast model named trustworthy-based estafet multi-point relays (TEMPR). This model is designed to minimise the communication overhead between peers while considering the trustworthiness of the peers such that only trustworthy peer may relay messages to other peers. With our approach, each peer is able to disseminate messages in the most efficient and reliable manner.

Trustworthy Group Making Algorithm in Distributed Systems

AbstractInformation systems are being shifted to scalable architectures like Cloud and peer-to-peer (P2P) models. In this paper, we consider the P2P model as a fully distributed, scalable system different from centralized coordinated systems in Cloud and Grid systems. A P2P system is composed of peer processes (peers). Here, applications are realized by activities of peers and cooperations among multiple peers. In P2P systems, since there is no centralized coordination, each peer has to obtain information about other peers by itself. In the group cooperation, each group member peer has to be trustworthy so that malicious behavior of a member peer cannot effect overall outcome of the whole group. Here, it is important to consider the trustworthiness of each group member as a base of an agreement procedure in the distributed environment. The goal of a group and the way to archive the goal are decided by the group members. During the agreement procedure, opinions of member peers have to be collected in a group. Malicious and unexpected behaviors of member peers can negatively effect the output of a group. Hence, it is significant to discuss how to compose a group only by including more trustworthy peers. In this paper, by taking advantage of the trustworthiness concept of each peer, we propose a novel approach to composing a trustworthy group in the distributed agreement protocols.

TMPR-scheme for reliably broadcast messages among peer processes

Nowadays information systems are being shifted to distributed architectures. The peer-to-peer (P2P) model as a fully distributed system, is composed of peer processes (peers). Here, peers have to efficiently and flexibly make an agreement on one common value which satisfies an agreement condition. In the agreement protocol, each peer has to deliver values to all the peers in a group. We take advantage of the multipoint relaying (MPR) mechanism to efficiently broadcast messages. Here, if a peer which forwards messages to other peers is faulty, the peers cannot receive messages. In this paper, we newly discuss a trustworthiness-based broadcast (TBB) algorithm where only the trustworthy peers forward messages. That is, untrustworthy peers, i.e., peers prone to faults and malicious behaviours do not forward messages. Here, the transmission fault implied by faulty peers can be reduced. We evaluate the TBB algorithm in terms of the number of messages compared with the pure flooding and MPR algorithms.

English

In Army Services, there are a number of valuable decisions that have to be taken for mission accomplishment. These decisions are very important and the choice of a weapon may be able to alter the outcome of a battle decisively. Among several such decisions one is to decide which weapons to deploy/assign over a given terrain. Recommender systems are intelligent applications to assist users in a decision-making process where they want to choose one item amongst a potentially overwhelming set of alternative products or services. This paper proposes the design of recommender system that automates the process of finding the appropriate type of weapon(s) that can be deployed over a terrain having certain characteristics. The user agent seeks recommendations, which are in the form of intuitionistic fuzzy set (IFS), from trustworthy peers and produces aggregated order of recommendations taking degree of trust on recommenders into consideration. Trust on recommender is also updated based on importance of recommendation given to the user. A prototype of the trust-based recommender system for modern tactical combat system has been designed and developed through which the user can get the recommendation to use a specific kind of weapon or a set of weapons that would be best-suited in a given type of terrain. Defence Science Journal, 2010, 60(4), pp.405-411 , DOI:http://dx.doi.org/10.14429/dsj.60.498

Trust algorithms in P2P file sharing networks

Many peer to peer (P2P) networks are used to share different types of files between users who are usually anonymous. To prevent the distribution of files that have been intentionally damaged, filled with false content or infected with malware, users need to be able to distinguish between trustworthy and malicious peers. One way to help a user in separating the good peers from the bad peers, is to provide him/her with a sophisticated trust algorithm that calculates the trustworthiness of any peer involved in a file sharing transaction. In this paper the results of a comparative study are presented as a first contribution. This study is based on taxonomy of 17 trust algorithms as found in literature. The evaluation is done by means of both a literature study and a series of simulation tests. By using the evaluation results, the advantages of each of the current algorithms are singled out, and next combined to develop and implement a new and better trust algorithm for file sharing in P2P networks. This is the second contribution.

Peer-to-peer’s most wanted: Malicious peers

In this paper, we propose a reputation management scheme for partially decentralized peer-to-peer systems. The reputation scheme helps to build trust among peers based on their past experiences and feedback from other peers. Two selection advisor algorithms are proposed for helping peers to select the most trustworthy peer to download from. The proposed algorithms can detect malicious peers sending inauthentic files. The Malicious detector algorithm is also proposed to detect liar peers that send the wrong feedback to subvert the reputation system. The new concept of suspicious transactions is introduced and explained. Simulation results confirm the capability of the proposed algorithms to effectively detect malicious peers and isolate them from the system, hence reducing the amount of inauthentic uploads, increasing peers’ satisfaction, and preserving network resources.