Reputation Systems Research Articles

Trusted reputation system for heterogeneous network resource sharing based on blockchain in IoT

Trusted reputation system for heterogeneous network resource sharing based on blockchain in IoT

Enhancing Security in Social Networks through Machine Learning: Detecting and Mitigating Sybil Attacks with SybilSocNet

This study contributes to the Sybil node-detecting algorithm in online social networks (OSNs). As major communication platforms, online social networks are significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. An additional exploration of distinct reputation systems and their practical applications led to this study’s discovery of machine learning algorithms, i.e., the KNN, support vector machine, and random forest algorithms, as part of our SybilSocNet. This study details the data-cleansing process for the employed dataset for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to an explanation and analysis of our conducted experiments and comparing their results. The experiments demonstrated the algorithm’s ability to detect Sybil nodes in OSNs (99.9% accuracy in SVM, 99.6% in random forest, and 97% in KNN algorithms), and we propose future research opportunities.

Strategic Insights for Sustainable Tourism Development in Kashmir Valley: SWOT and QSPM Analysis

Promoting sustainable tourism entails fostering environmentally responsible growth that endures, achieving a delicate balance between honoring the rich history, preserving culture, and navigating the challenges posed by diverse altitudes in the Kashmir Valley. A knowledge test was created to measure the stakeholders' understanding of sustainable tourism to guide further analysis with the selected stakeholders. Our research delved into this objective by conducting real-world analysis, including questionnaires and interviews with key stakeholders. A Quantitative Strategic Planning Matrix (QSPM) was utilized and built on the foundation of the SWOT (Strengths, Weaknesses, Opportunities, and Threats) framework to drive sustainable tourism effectively. The results underscore the intricate landscape the valley operates within, revealing varied levels of stakeholder satisfaction across different aspects. Understanding these nuanced perspectives is crucial to charting a sustainable course for the region. In this light, the existing strategies aimed at managing the area, with a focal point on promoting sustainable tourism. The emphasis is on recognizing and tackling the challenges while seizing the weaknesses and threats that could impact the industry's reputation for excellent service and environmental stewardship. It is about aligning strategies to ensure a harmonious coexistence between tourism growth and the natural, cultural, and social fabric of this adorable valley.

Blockchain-based Reputation System for Smart Farm Anomaly Detection and Prevention

While smart farming is becoming increasingly popular as a way to improve the efficiency and sustainability of agroecosystems and global food security, its vulnerability to cyber-attacks is rapidly increasing. A distributed reputation system can help detect and prevent malicious activities by tracking the reputation of IP addresses of devices, products, and entities in a smart farming system, thereby enhancing its security and trust. In this paper, we propose a blockchain-based reputation system for IP addresses in smart farms for real-time intrusion detection and prevention. The study uses the Ethereum blockchain smart contracts, Oracles, Intrusion Detection System (IDS), and a Proof-of-Reputation (PoR) consensus mechanism. The system was evaluated on two datasets, the Spamhaus Blocklist, and the Malware Domain List datasets. It achieved high accuracy, recall, and precision rates, F1 score, as well as low false positive and false negative rates. This shows that the proposed system has high potential to be an effective tool for improving malware detection and prevention in real-time, creating a trusted supply chain for a smart farming ecosystem against a wide range of attack types, including denial-of-service attacks, probe attacks, and user-to-root attacks.

Trusting the Untrustworthy: How trust is established and maintained in illicit markets and organised crime groups

Illicit markets and organised crime groups are characterised by risk and uncertainty. Inherent information asymmetry and the ubiquitous threat of law enforcement and adversaries, like scammers, create an environment that where cooperation and exchange would seem unlikely. To profit in illicit markets, however, cooperation is necessary and, given the potential payoffs, illicit actors find means to overcome the barriers to collaboration. This article explores how criminal actors cooperate in what would be assumed to be an uncooperative environment. Without the usual legal mechanisms, they establish trust to mitigate against such risks. This is done by leveraging social capital, emitting signals that are cheap to emit but costly to fake and using the threat of violent sanctions. In online markets, where anonymity generates further problems for cooperation, actors also use built-in reputation systems and chat forums to mitigate risk.

A Privacy-Preserving and Quality-Aware User Selection Scheme for IoT

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users’ data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency.

Reviews, trust, and customer experience in online marketplaces: the case of Mercado Libre Colombia

PurposeThe research focuses on the crucial role of online reviews in shaping consumer trust in e-commerce platforms, examining the impact of perceived authentic and fake reviews on purchasing decisions and platform reputation. It assesses how consumers perceive review authenticity and quality and their effects on trust levels in reviews, marketplaces, and reputation systems. It also explores the relationship between trust forms and overall experiences.Design/methodologyA quantitative approach is employed, utilizing a questionnaire distributed to recent Mercado Libre buyers. To test hypotheses, data from 326 valid responses are analyzed using confirmatory factor analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM).FindingsFindings reveal that fake review perception negatively affects trust in rating systems, while high-quality reviews positively influence all trust forms. Customer experience is directly impacted by trust in marketplaces and rating systems, indicating a mediation effect of trust in the rating system on the relationship between fake review perception and customer experience.Research limitations/implicationsLimitations include using a convenience sample and focusing on trust in the rating system rather than reviews or the marketplace, suggesting avenues for future research. Practical implications include recommendations to ensure review quality, enhance rating system controls, and promote review usage in the purchase process.OriginalityThe study addresses a timely and relevant gap in understanding the impact of reviews on e-commerce trust, particularly within the context of Latin America and Mercado Libre’s dominance in the region’s e-commerce landscape.

Drivers of Knowledge Sharing in Virtual Brand Communities: Self-Determination Perspective

ABSTRACT The prevalence of virtual brand communities and the knowledge sharing occurring throughout these populations has become a staple of both business and consumer online realities. Expanding work on personal motivations, and guided by self-determination theory, this research explores autonomous and relatedness motivations that drive individual knowledge sharing within online communities. The authors argue that such knowledge sharing can be fostered by many drivers, including self-presentation factors, brand community engagement elements, user attitude toward the brand, user reputation, size of community network, and reciprocity from other community members. Analysis was conducted using data collected longitudinally from 2866 club members of Xiaomi’s online MI User Interface community in China. Results reveal that personal signature, post length, online time, login frequency, brand attitude, reputation, online social network, and reciprocity all positively affect individual knowledge sharing within the virtual brand community, while the use of picture posts may have adverse effects and impact knowledge sharing negatively.

A mixed method study exploring similarities and differences in general and social services-specific barriers to treatment-seeking among individuals with a problematic use of alcohol, cannabis, or gambling

IntroductionThe treatment gap for addictive disorders is one of the largest in health care. Several studies have investigated barriers to treatment for different addictive disorders, but very few studies conducted have explored whether the barriers differ depending on substance or behavior or if they are common among all addictive disorders. In Sweden, addiction care is provided both by the healthcare and social services, where the latter is common, but also less popular. To our knowledge, there are no studies exploring whether the barriers are different depending on where the treatment is given.AimThe aim was to thoroughly explore both which general and social services-specific barriers to treatment that are common, which barriers that differs, and how the barriers are described among individuals with a problematic use of alcohol, cannabis and/or gambling.MethodA mixed method convergent parallel design was conducted. For the quantitative measures, surveys including the validated Barriers to Treatment Inventory as well as questions regarding barriers in the Swedish multi-provider landscape, were collected from individuals with a problematic use of alcohol (n = 207), cannabis (n = 51), and gambling (n = 37). In parallel, 17 semi-structured interviews from the same population were conducted and analyzed with thematic analysis. Thereafter, the quantitative and qualitative data was compared, contrasted, and at last, interpreted.ResultsThe quantitative data showed that the largest general barriers in all groups were privacy concern and poor availability, and the largest barriers for seeking help from the social services was stigma, unawareness of what is offered, and fear of consequences for all groups. The qualitative data resulted in five general barriers: stigma, ambivalence, accessibility, fear of consequences, and lack of knowledge about addiction and its’ treatments, and three barriers specifically towards social services: social services reputation, fear of meeting acquaintances, and lack of knowledge. The themes were developed from data from all groups, but different aspects of the themes were mentioned by different groups.ConclusionThere are details and aspects that differentiates both the general and social service-specific barriers to treatment between individuals with a problematic use of alcohol, cannabis, and gambling, but in large they perceive similar barriers.

BSPM: Blockchain-based Security Protection Model for power load management terminals

Power load management terminals are essential components of the smart grid system. However, they encounter numerous security challenges owing to their widespread deployment across large areas. These nodes exist in uncontrollable physical environments and are vulnerable to malicious attacks. Researching security measures for these terminals is vital to enhancing the security of the smart grid. Blockchain technology, with its distributed storage and tamper-proof features, can effectively protect the data security of these terminals. This paper proposes a blockchain-based security protection model for power load management terminals. First, the architecture of the model is designed using blockchain technology. In addition, a dynamic trust evaluation method for terminals is established through the beta reputation system. The blockchain consensus algorithm for power load management terminals is designed based on the trust evaluation value and practical Byzantine fault tolerance. The effectiveness of the trust evaluation model is verified by simulating the terminal operating situation. The message complexity and storage requirements of the security protection model are analyzed by comparing them with results from related studies.

Revolutionizing machine learning: Blockchain-based crowdsourcing for transparent and fair labeled datasets supply

With the development of 5G/6G communication networks, the industrial Internet of Things (IIoT) industry has generated a massive amount of data, presenting opportunities for advancements in the field of machine learning. The core of machine learning, labeled datasets, requires qualities such as diversity, quantity, and quality. However, the current collection of training datasets is mostly centralized, relying on crowdsourcing systems as platforms. Hence, there are four open challenges existing: (1) traditional crowdsourcing systems are mostly based on centralized platform which often suffers single point of failure, mischief attacks, DDoS attacks and are easy to be remotely hijacked; (2) the transparency of traditional crowdsourcing systems cannot be guaranteed, which results in the unfairness in assigning task or rewarding worker; (3) the quality of labeled training dataset cannot be guaranteed as the workers in traditional crowdsourcing systems are professional or non-professional; (4) the privacy of labels cannot be preserved as the platform can also learn the labeled result. In this paper, we utilize web 3.0 technology to propose a blockchain-based labeled training dataset supply system which can simultaneously supply annotation service and labeled training dataset with the challenges above overcome. Meanwhile we design a privacy-preserved truth discovery suitable for categorical data by combining it with Software Guard Extensions (SGX). Furthermore, we design a fair rewards distribution mechanism which is based on reputation system and Shapley value. The two mechanisms above can ensure the quality of labeled training dataset. Finally, to demonstrate the practicability of our design, we implement a prototype of the system deployed on Fabric test network and conduct extensive simulations. Compared to applications on public chains, the throughput of our blockchain application can reach 53 times higher.

Alguém se meteu no meio: modelos de intermediação digital do trabalho doméstico no Brasil

Informality is a reality for three-quarters of domestic workers in Brazil who, without a formal employment relationship, are not protected by the legal guarantees established since 2013. This lack of legal protection is exacerbated by the emphasis on entrepreneurship promoted by a growing number of digital platforms offering domestic work intermediation. In this article, we examine the structural standards of digital intermediation companies for domestic work in Brazil. Four representative platforms (GetNinjas, Parafuzo, Helpty, and Crafty) were analyzed. Our research corpus consists of their websites and applications, their terms of use, as well as journalistic articles and complaints from workers and clients on the website Reclame Aqui. The results are presented in three parts: (i) the history and general characteristics of each platform; (ii) their content organized into four categories of analysis (marketing, hiring, remuneration, and reputation systems); and (iii) the accounts of clients and workers on the Reclame Aqui website. The study found that digital intermediaries for domestic work present complex challenges for workers, as they amplify existing risks in the dominant labor model, such as intense subordination, economic dependence, lack of transparency about the value of their work, and exclusion from legal labor protection, whose recent expansion has not resulted in effective change. The study also suggests that this intensification of risks increases directly with the degree of intervention by the digital intermediary in the client-worker relationship.

A Systematic Review of Blockchain-based Privacy-Preserving Reputation Systems for IoT Applications

A Systematic Review of Blockchain-based Privacy-Preserving Reputation Systems for IoT Applications

Cloud Manufacturing Service Composition Optimization Based on Improved Chaos Sparrow Search Algorithm with Time-Varying Reliability and Credibility Evaluation

The economic friction and political conflicts between some countries and regions have made multinational corporations increasingly focus on the reliability and credibility of manufacturing supply chains. In view of the impact of poor manufacturing entity reliability and service reputation on the new-era manufacturing industry, the time-varying reliability and time-varying credibility of cloud manufacturing (CMfg) services were studied from the perspective of combining nature and society. Taking time-varying reliability, time-varying credibility, composition complexity, composition synergy, execution time, and execution cost as objective functions, a new six-dimension comprehensive evaluation model of service quality was constructed. To solve the optimization problem, this study proposes an improved chaos sparrow search algorithm (ICSSA), where the Bernoulli chaotic mapping formula was introduced to improve the basic sparrow search algorithm (BSSA), and the position calculation formulas of the explorer sparrow and the scouter sparrow were enhanced. The Bernoulli chaotic operator changed the symmetry of the BSSA, increased the uncertainty and randomness of the explorer sparrow position in the new algorithm, and affected the position update and movement strategies of the follower and scouter sparrows. The asymmetric chaotic characteristic brought better global search ability and optimization performance to the ICSSA. The comprehensive performance of the service composition (SvcComp) scheme was evaluated by calculating weighted relative deviation based on six evaluation elements. The WFG and DTLZ series test functions were selected, and the inverse generation distance (IGD) index and hyper volume (HV) index were used to compare and evaluate the convergence and diversity of the ICSSA, BSSA, PSO, SGA, and NSGA-III algorithms through simulation analysis experiments. The test results indicated that the ICSSA outperforms the BSSA, PSO, SGA, and NSGA-III in the vast majority of testing issues. Finally, taking disinfection robot manufacturing tasks as an example, the effectiveness of the proposed CMfg SvcComp optimization model and the ICSSA were verified. The case study results showed that the proposed ICSSA had faster convergence speed and better comprehensive performance for the CMfg SvcComp optimization problem compared with the BSSA, PSO, SGA, and NSGA-III.

Reputation System as a Digital Socio-Economic Institution: Willingness to Sell in Online Markets

This study examines the features and big data consisting of digital trace records from economic actions within Bukalapak's reputation system and their impact on the willingness to sell (WTS) electronic products. Digital mixed content analysis was used to analyze the rapidly evolving digital-social-based economic institution infrastructure. Data were collected, analyzed, and visualized using Python and analyzed using the concepts of new economic institutions and WTS. The findings indicate that the reputation system is a formal element comprising algorithms, rules, scripts, and patterns. This system facilitates social processes through feedback mechanisms in product descriptions, comments, reviews, ratings, and badges, which generate informal elements such as norms, trust, and symbolic values. These two elements are interconnected into an institutional framework whose function is to ensure that the market, characterized by high levels of anonymous transactions, uncertainty, and asymmetric information, can remain stable and meet the expectations of the actors involved.

A Survey on Reputation Systems for UAV Networks

The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within the highly dynamic and adaptable environments of UAV networks. Decentralized reputation systems have emerged as a promising solution for enhancing the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. The crucial role of reputation systems is to strengthen trust, security, and privacy throughout these networks, and various strategies can be incorporated to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

A variational formulation for a trust and reputation system

A variational formulation for a trust and reputation system

Service Reputation and Customer Loyalty in Ocean Freight Forwarders: An Exporters’ Perspective in Mindanao Regions

Customer loyalty is a vital component for businesses to achieve success and growth and that gaining customer loyalty can be sustainable by building a positive reputation. This study investigated the influence of customer satisfaction and trust as dimensions of the reputation of Ocean Freight Forwarders on customer loyalty. Data were collected from 180 exporting companies in Mindanao and a survey questionnaire was administered. A descriptive correlational design was employed, and the results showed positive and statistically significant relationship between customer loyalty, satisfaction, and trust. The findings revealed that exporters perceive ocean freight forwarders' reputation as highly satisfactory in terms of technical and functional qualities, and they have a very high level of trust in forwarders' benevolence and credibility. The results also showed strong loyalty in all dimensions of customer loyalty. Thus, the practical implications of this study are clear: prioritizing customer satisfaction and maintaining trust by the forwarders are essential for establishing a strong reputation. Therefore, ocean freight forwarders should consider these findings and continuously evaluate and improve their trust image and customer satisfaction strategies to enhance customer loyalty and maintain a competitive position in the dynamic export industry.

ARS-Chain: A Blockchain-Based Anonymous Reputation-Sharing Framework for E-Commerce Platforms

E-commerce platforms incorporate reputation systems that allow buyers to rate sellers after transactions. However, existing reputation systems face challenges such as privacy leakage, linkability, and multiple rating attacks. The feedback data can inadvertently expose user information privacy because they reveal the buyers’ identities and preferences, which deters a significant number of users from providing their ratings. Moreover, malicious actors can exploit data analysis and machine learning techniques to mine user privacy from the rating data, posing serious threats to user security and trust. This study introduces ARS-Chain, a pioneering and secure blockchain-driven anonymous reputation-sharing framework tailored for e-commerce platforms. The core of ARS-Chain is a dynamic ring addition mechanism with linkable ring signatures (LRS), where the number of LRS rings is dynamically added in alignment with the evolving purchase list, and LRS link tags are constructed with the LRS rings and item identifiers. Further, a consortium blockchain is introduced to store these anonymous ratings on e-commerce platforms. As a result, ARS-Chain ensures full anonymity while achieving cross-platform reputation sharing, making rating records unlinkable, and effectively countering multiple rating attacks. The experimental results confirm that ARS-Chain significantly enhances user information privacy protection while maintaining system performance, having an important impact on the construction of trust mechanisms for e-commerce platforms.

Bengali fake reviews: A benchmark dataset and detection system

The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.