Relevant bibliographies by topics / Trust inference

Academic literature on the topic 'Trust inference'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Trust inference"

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Alahmadi, Dimah, and Xaio-Jun Zeng. "Improving Recommendation Using Trust and Sentiment Inference from OSNs." International Journal of Knowledge Engineering-IACSIT 1, no. 1 (2015): 9–17. http://dx.doi.org/10.7763/ijke.2015.v1.2.

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Orman, Levent V. "Bayesian Inference in Trust Networks." ACM Transactions on Management Information Systems 4, no. 2 (August 2013): 1–21. http://dx.doi.org/10.1145/2489790.

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Li, Lei, and Yan Wang. "Subjective Trust Inference in Composite Services." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1377–84. http://dx.doi.org/10.1609/aaai.v24i1.7504.

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In Service-Oriented Computing (SOC) environments, the trustworthiness of each service is critical for a service client when selecting one from a large pool of services. The trust value of a service is usually in the range of [0,1] and is evaluated from the ratings given by service clients, which represent the subjective belief of these service clients on the satisfaction of delivered services. So a trust value can be taken as the subjective probability, with which one party believes that another party can perform an action in a certain situation. Hence, subjective probability theory should be adopted in trust evaluation. In addition, in SOC environments, a service usually invokes other services offered by different service providers forming a composite service. Thus, the global trust of a composite service should be evaluated based on complex invocation structures. In this paper, firstly, based on Bayesian inference, we propose a novel method to evaluate the subjective trustworthiness of a service component from a series of ratings given by service clients. Secondly, we interpret the trust dependency caused by service invocations as conditional probability, which is evaluated based on the subjective trust values of service components. Furthermore, we propose a joint subjective probability method to evaluate the subjective global trust of a composite service on the basis of trust dependency. Finally, we introduce the results of our conducted experiments to illustrate the properties of our proposed subjective global trust inference method.
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Liu, Yu, and Bai Wang. "User Trust Inference in Online Social Networks: A Message Passing Perspective." Applied Sciences 12, no. 10 (May 20, 2022): 5186. http://dx.doi.org/10.3390/app12105186.

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Online social networks are vital environments for information sharing and user interactivity. To help users of online social services to build, expand, and maintain their friend networks or webs of trust, trust management systems have been deployed and trust inference (or more generally, friend recommendation) techniques have been studied in many online social networks. However, there are some challenging issues obstructing the real-world trust inference tasks. Using only explicit yet sparse trust relationships to predict user trust is inefficient in large online social networks. In the age of privacy-respecting Internet, certain types of user data may be unavailable, and thus existing models for trust inference may be less accurate or even defunct. Although some less interpretable models may achieve better performance in trust prediction, the interpretability of the models may prevent them from being adopted or improved for making relevant informed decisions. To tackle these problems, we propose a probabilistic graphical model for trust inference in online social networks in this paper. The proposed model is built upon the skeleton of explicit trust relationships (the web of trust) and embeds various types of available user data as comprehensively-designed trust-aware features. A message passing algorithm, loop belief propagation, is applied to the model inference, which greatly improves the interpretability of the proposed model. The performance of the proposed model is demonstrated by experiments on a real-world online social network dataset. Experimental results show the proposed model achieves acceptable accuracy with both fully and partially available data. Comparison experiments were conducted, and the results show the proposed model’s promise for trust inference in some circumstances.
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Nevill, Alan M., A. Mark Williams, Colin Boreham, Eric S. Wallace, Gareth W. Davison, Grant Abt, Andrew M. Lane, and Edward M. Winter. "Can we trust “Magnitude-based inference”?" Journal of Sports Sciences 36, no. 24 (November 4, 2018): 2769–70. http://dx.doi.org/10.1080/02640414.2018.1516004.

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Wang, Guojun, and Jie Wu. "FlowTrust: trust inference with network flows." Frontiers of Computer Science in China 5, no. 2 (May 9, 2011): 181–94. http://dx.doi.org/10.1007/s11704-011-0323-4.

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Yao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Pairwise trust inference by subgraph extraction." Social Network Analysis and Mining 3, no. 4 (October 6, 2013): 953–68. http://dx.doi.org/10.1007/s13278-013-0140-x.

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Denève, Sophie, and Renaud Jardri. "Circular inference: mistaken belief, misplaced trust." Current Opinion in Behavioral Sciences 11 (October 2016): 40–48. http://dx.doi.org/10.1016/j.cobeha.2016.04.001.

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Wang, Chenlan, Chongjie Zhang, and X. Jessie Yang. "Automation reliability and trust: A Bayesian inference approach." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 202–6. http://dx.doi.org/10.1177/1541931218621048.

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Research shows that over repeated interactions with automation, human operators are able to learn how reliable the automation is and update their trust in automation. The goal of the present study is to investigate if this learning and inference process approximately follow the principle of Bayesian probabilistic inference. First, we applied Bayesian inference to estimate human operators’ perceived system reliability and found high correlations between the Bayesian estimates and the perceived reliability for the majority of the participants. We then correlated the Bayesian estimates with human operators’ reported trust and found moderate correlations for a large portion of the participants. Our results suggest that human operators’ learning and inference process for automation reliability can be approximated by Bayesian inference.
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Lesani, Mohsen, and Niloufar Montazeri. "FUZZY TRUST AGGREGATION AND PERSONALIZED TRUST INFERENCE IN VIRTUAL SOCIAL NETWORKS." Computational Intelligence 25, no. 2 (May 2009): 51–83. http://dx.doi.org/10.1111/j.1467-8640.2009.00334.x.

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Dissertations / Theses on the topic "Trust inference"

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Bhuiyan, Touhid. "Trust-based automated recommendation making." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/49168/1/Touhid_Bhuiyan_Thesis.pdf.

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Recommender systems are one of the recent inventions to deal with ever growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbours, generated from a database made up of the preferences of past users. With sufficient background information of item ratings, its performance is promising enough but research shows that it performs very poorly in a cold start situation where there is not enough previous rating data. As an alternative to ratings, trust between the users could be used to choose the neighbour for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world "friend of a friend" recommendations. To extend the boundaries of the neighbour, an effective trust inference technique is required. This thesis proposes a trust interference technique called Directed Series Parallel Graph (DSPG) which performs better than other popular trust inference algorithms such as TidalTrust and MoleTrust. Another problem is that reliable explicit trust data is not always available. In real life, people trust "word of mouth" recommendations made by people with similar interests. This is often assumed in the recommender system. By conducting a survey, we can confirm that interest similarity has a positive relationship with trust and this can be used to generate a trust network for recommendation. In this research, we also propose a new method called SimTrust for developing trust networks based on user's interest similarity in the absence of explicit trust data. To identify the interest similarity, we use user's personalised tagging information. However, we are interested in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbours used in the automated recommender system. Our experimental results show that our proposed tag-similarity based method outperforms the traditional collaborative filtering approach which usually uses rating data.
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Varrier, Rekha Sreekumar. "Never Trust the Teller! Feedback Manipulation and its Impact on Perceptual Inference." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21259.

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Laut der „Bayesian Brain“-Hypothese ist Wahrnehmung ein Inferenzprozess, der von sensorischen Daten abhängt, aber auch von Vorannahmen über die Wahrscheinlichkeit sensorischer Daten und ihrer Zuverlässigkeit. Feedback aus der Umgebung verbessert das Lernen und hilft dem Gehirn, diesen Inferenzprozess zu optimieren. In vorherigen Arbeiten wurde gezeigt, dass unzuverlässiges Feedback die perzeptuelle Genauigkeit beeinträchtigt und Fehlwahrnehmungen in Rauschsignalen erhöht. In dieser Arbeit wurde die Hypothese untersucht, dass der Effekt von unzuverlässigem Feedback einer geringeren Gewichtung sensorischer Daten im Inferenzprozess entspricht. Hierzu wurden zwei Studien mit visuellen Reizen durchgeführt: Studie I umfasste zwei Verhaltensexperimente; Studie II umfasste ein Experiment mit funktioneller Magnetresonanztomographie. Unter der Annahme einer Abwertung sensorischer Information infolge unzuverlässigen Feedbacks wurde eine Verringerung der perzeptuellen Leistung vorhergesagt und eine Verschiebung der Wahrnehmungsinferenz zu experimentell induzierten Vorannahmen. Auf neuronaler Ebene wurde untersucht, ob sich sensorische Repräsentationen im primären visuellen Kortex (V1) als Folge unzuverlässigen Feedbacks verschlechtern würden. In allen Experimenten wurde in einer Kontrollbedingung zuverlässiges Feedback gegeben. Die Ergebnisse beider Studien zeigten eine Abnahme der perzeptuellen Leistung nach unzuverlässigem versus zuverlässigem Feedback. Darüber hinaus verließen sich die Probanden zunehmend auf induzierte Vorannahmen. Auf neuronaler Ebene zeigte sich eine Verrauschung sensorischer Repräsentationen in V1 als Folge unzuverlässigen Feedbacks. Zusammenfassend zeigt sich, dass die Induzierung von Überzeugungen über die Zuverlässigkeit sensorischer Informationen durch manipuliertes Leistungsfeedback einen systematischen Einfluss auf perzeptuelle Inferenz hat und dass sich diese Veränderungen in frühen sensorischen Arealen manifestieren.
According to the Bayesian brain hypothesis, perception is an inferential process that depends not only on sensory data, but also on our beliefs about likely sensory data and their reliability. Feedback from the environment improves this inferential process. Indeed previous studies have shown that unreliable feedback impairs task performance and increases illusory pattern perception in noise. In this thesis, we explored the hypothesis that the effect of unreliable feedback is a down-weighting of sensory information in perceptual inference. We conducted two studies comprising visual stimuli: Study I comprised two behavioural experiments and Study II comprised a functional magnetic resonance imaging experiment. Based on the hypothesis that sensory data would be down-weighed after unreliable feedback , we predicted that perceptual performance would deteriorate and that perceptual inference would shift towards experimentally induced priors. Further, we investigated whether the sensory data representations in the primary visual cortex (V1) deteriorate as a result of unreliable feedback. Reliable feedback was used as a control condition in all the experiments. Data from both studies demonstrated that performance did decrease following unreliable feedback compared to reliable feedback. Moreover, observers increasingly relied on prior information as the feedback about their percepts became unreliable. At the neural level, low-level stimulus representations deteriorated in V1 with unreliable feedback. To sum up, our results show that inducing beliefs about the reliability of sensory information by manipulating performance feedback can systematically influence perceptual inference and that these changes manifest at the earliest stages of cortical sensory processing.
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Kramdi, Seifeddine. "A modal approach to model computational trust." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30146/document.

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Le concept de confiance est un concept sociocognitif qui adresse la question de l'interaction dans les systèmes concurrents. Quand la complexité d'un système informatique prohibe l'utilisation de solutions traditionnelles de sécurité informatique en amont du processus de développement (solutions dites de type dur), la confiance est un concept candidat, pour le développement de systèmes d'aide à l'interaction. Dans cette thèse, notre but majeur est de présenter une vue d'ensemble de la discipline de la modélisation de la confiance dans les systèmes informatiques, et de proposer quelques modèles logiques pour le développement de module de confiance. Nous adoptons comme contexte applicatif majeur, les applications basées sur les architectures orientées services, qui sont utilisées pour modéliser des systèmes ouverts telle que les applications web. Nous utiliserons pour cela une abstraction qui modélisera ce genre de systèmes comme des systèmes multi-agents. Notre travail est divisé en trois parties, la première propose une étude de la discipline, nous y présentons les pratiques utilisées par les chercheurs et les praticiens de la confiance pour modéliser et utiliser ce concept dans différents systèmes, cette analyse nous permet de définir un certain nombre de points critiques, que la discipline doit aborder pour se développer. La deuxième partie de notre travail présente notre premier modèle de confiance. Cette première solution basée sur un formalisme logique (logique dynamique épistémique), démarre d'une interprétation de la confiance comme une croyance sociocognitive, ce modèle présentera une première modélisation de la confiance. Apres avoir prouvé la décidabilité de notre formalisme. Nous proposons une méthodologie pour inférer la confiance en des actions complexes : à partir de notre confiance dans des actions atomiques, nous illustrons ensuite comment notre solution peut être mise en pratique dans un cas d'utilisation basée sur la combinaison de service dans les architectures orientées services. La dernière partie de notre travail consiste en un modèle de confiance, où cette notion sera perçue comme une spécialisation du raisonnement causal tel qu'implémenté dans le formalisme des règles de production. Après avoir adapté ce formalisme au cas épistémique, nous décrivons trois modèles basés sur l'idée d'associer la confiance au raisonnement non monotone. Ces trois modèles permettent respectivement d'étudier comment la confiance est générée, comment elle-même génère les croyances d'un agent et finalement, sa relation avec son contexte d'utilisation
The concept of trust is a socio-cognitive concept that plays an important role in representing interactions within concurrent systems. When the complexity of a computational system and its unpredictability makes standard security solutions (commonly called hard security solutions) inapplicable, computational trust is one of the most useful concepts to design protocols of interaction. In this work, our main objective is to present a prospective survey of the field of study of computational trust. We will also present two trust models, based on logical formalisms, and show how they can be studied and used. While trying to stay general in our study, we use service-oriented architecture paradigm as a context of study when examples are needed. Our work is subdivided into three chapters. The first chapter presents a general view of the computational trust studies. Our approach is to present trust studies in three main steps. Introducing trust theories as first attempts to grasp notions linked to the concept of trust, fields of application, that explicit the uses that are traditionally associated to computational trust, and finally trust models, as an instantiation of a trust theory, w.r.t. some formal framework. Our survey ends with a set of issues that we deem important to deal with in priority in order to help the advancement of the field. The next two chapters present two models of trust. Our first model is an instantiation of Castelfranchi & Falcone's socio-cognitive trust theory. Our model is implemented using a Dynamic Epistemic Logic that we propose. The main originality of our solution is the fact that our trust definition extends the original model to complex action (programs, composed services, etc.) and the use of authored assignment as a special kind of atomic actions. The use of our model is then illustrated in a case study related to service-oriented architecture. Our second model extends our socio-cognitive definition to an abductive framework that allows us to associate trust to explanations. Our framework is an adaptation of Bochman's production relations to the epistemic case. Since Bochman approach was initially proposed to study causality, our definition of trust in this second model presents trust as a special case of causal reasoning, applied to a social context. We end our manuscript with a conclusion that presents how we would like to extend our work
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Ayday, Erman. "Iterative algorithms for trust and reputation management and recommender systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/45868.

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This thesis investigates both theoretical and practical aspects of the design and analysis of iterative algorithms for trust and reputation management and recommender systems. It also studies the application of iterative trust and reputation management mechanisms in ad-hoc networks and P2P systems. First, an algebraic and iterative trust and reputation management scheme (ITRM) is proposed. The proposed ITRM can be applied to centralized schemes, in which a central authority collects the reports and forms the reputations of the service providers (sellers) as well as report/rating trustworthiness of the (service) consumers (buyers). It is shown that ITRM is robust in filtering out the peers who provide unreliable ratings. Next, the first application of Belief Propagation algorithm, a fully iterative probabilistic algorithm, on trust and reputation management (BP-ITRM) is proposed. In BP-ITRM, the reputation management problem is formulated as an inference problem, and it is described as computing marginal likelihood distributions from complicated global functions of many variables. However, it is observed that computing the marginal probability functions is computationally prohibitive for large scale reputation systems. Therefore, the belief propagation algorithm is utilized to efficiently (in linear complexity) compute these marginal probability distributions. In BP-ITRM, the reputation system is modeled by using a factor graph and reputation values of the service providers (sellers) are computed by iterative probabilistic message passing between the factor and variable nodes on the graph. It is shown that BP-ITRM is reliable in filtering out malicious/unreliable reports. It is proven that BP-ITRM iteratively reduces the error in the reputation values of service providers due to the malicious raters with a high probability. Further, comparison of BP-ITRM with some well-known and commonly used reputation management techniques (e.g., Averaging Scheme, Bayesian Approach and Cluster Filtering) indicates the superiority of the proposed scheme both in terms of robustness against attacks and efficiency. The introduction of the belief propagation and iterative message passing methods onto trust and reputation management has opened up several research directions. Thus, next, the first application of the belief propagation algorithm in the design of recommender systems (BPRS) is proposed. In BPRS, recommendations (predicted ratings) for each active user are iteratively computed by probabilistic message passing between variable and factor nodes in a factor graph. It is shown that as opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all users if it wishes to update the recommendations for only a single active user using the most recent data (ratings). Further, BPRS computes the recommendations for each user with linear complexity, without requiring a training period while it remains comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. This work also explores fundamental research problems related to application of iterative and probabilistic reputation management systems in various fields (such as ad-hoc networks and P2P systems). A distributed malicious node detection mechanism is proposed for delay tolerant networks (DTNs) using ITRM which enables every node to evaluate other nodes based on their past behavior, without requiring a central authority. Further, for the first time. the belief propagation algorithm is utilized in the design and evaluation of distributed trust and reputation management systems for P2P networks. Several schemes are extensively simulated and are compared to demonstrate the effectiveness of the iterative algorithms and belief propagation on these applications.
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Alhaqbani, Bandar Saleh. "Privacy and trust management for electronic health records." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/37635/1/Bandar_Alhaqbani_Thesis.pdf.

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Establishing a nationwide Electronic Health Record system has become a primary objective for many countries around the world, including Australia, in order to improve the quality of healthcare while at the same time decreasing its cost. Doing so will require federating the large number of patient data repositories currently in use throughout the country. However, implementation of EHR systems is being hindered by several obstacles, among them concerns about data privacy and trustworthiness. Current IT solutions fail to satisfy patients’ privacy desires and do not provide a trustworthiness measure for medical data. This thesis starts with the observation that existing EHR system proposals suer from six serious shortcomings that aect patients’ privacy and safety, and medical practitioners’ trust in EHR data: accuracy and privacy concerns over linking patients’ existing medical records; the inability of patients to have control over who accesses their private data; the inability to protect against inferences about patients’ sensitive data; the lack of a mechanism for evaluating the trustworthiness of medical data; and the failure of current healthcare workflow processes to capture and enforce patient’s privacy desires. Following an action research method, this thesis addresses the above shortcomings by firstly proposing an architecture for linking electronic medical records in an accurate and private way where patients are given control over what information can be revealed about them. This is accomplished by extending the structure and protocols introduced in federated identity management to link a patient’s EHR to his existing medical records by using pseudonym identifiers. Secondly, a privacy-aware access control model is developed to satisfy patients’ privacy requirements. The model is developed by integrating three standard access control models in a way that gives patients access control over their private data and ensures that legitimate uses of EHRs are not hindered. Thirdly, a probabilistic approach for detecting and restricting inference channels resulting from publicly-available medical data is developed to guard against indirect accesses to a patient’s private data. This approach is based upon a Bayesian network and the causal probabilistic relations that exist between medical data fields. The resulting definitions and algorithms show how an inference channel can be detected and restricted to satisfy patients’ expressed privacy goals. Fourthly, a medical data trustworthiness assessment model is developed to evaluate the quality of medical data by assessing the trustworthiness of its sources (e.g. a healthcare provider or medical practitioner). In this model, Beta and Dirichlet reputation systems are used to collect reputation scores about medical data sources and these are used to compute the trustworthiness of medical data via subjective logic. Finally, an extension is made to healthcare workflow management processes to capture and enforce patients’ privacy policies. This is accomplished by developing a conceptual model that introduces new workflow notions to make the workflow management system aware of a patient’s privacy requirements. These extensions are then implemented in the YAWL workflow management system.
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Varrier, Rekha Sreekumar [Verfasser], Martin [Gutachter] Rolfs, Florian [Gutachter] Schlagenhauf, and Philipp [Gutachter] Sterzer. "Never Trust the Teller! Feedback Manipulation and its Impact on Perceptual Inference / Rekha Sreekumar Varrier ; Gutachter: Martin Rolfs, Florian Schlagenhauf, Philipp Sterzer." Berlin : Humboldt-Universität zu Berlin, 2020. http://d-nb.info/1206587792/34.

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Ben-Mosbah, Azza. "Privacy-preserving spectrum sharing." Thesis, Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0008/document.

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Les bandes des fréquences, telles qu'elles sont aménagées aujourd'hui, sont statiquement allouées. Afin d'améliorer la productivité et l'efficacité de l'utilisation du spectre, une nouvelle approche a été proposée : le "partage dynamique du spectre". Les régulateurs, les industriels et les scientifiques ont examiné le partage des bandes fédérales entre les détenteurs de licences (utilisateurs primaires) et les nouveaux entrants (utilisateurs secondaires). La nature d'un tel partage peut faciliter les attaques d'inférence et mettre en péril les paramètres opérationnels des utilisateurs primaires. Par conséquent, le but de cette thèse est d'améliorer la confidentialité des utilisateurs primaires tout en permettant un accès secondaire au spectre. Premièrement, nous présentons une brève description des règles de partage et des exigences en termes de confidentialité dans les bandes fédérales. Nous étudions également les techniques de conservation de confidentialité (obscurcissement) proposées dans les domaines d'exploration et d'édition de données pour contrecarrer les attaques d'inférence. Ensuite, nous proposons et mettons en œuvre notre approche pour protéger la fréquence et la localisation opérationnelles contre les attaques d'inférence. La première partie étudie la protection de la fréquence opérationnelle en utilisant un obscurcissement inhérent et explicite pour préserver la confidentialité. La deuxième partie traite la protection de la localisation opérationnelle en utilisant la confiance comme principale contre-mesure pour identifier et atténuer un risque d'inférence. Enfin, nous présentons un cadre axé sur les risques qui résume notre travail et s'adapte à d'autres approches de protection de la confidentialité. Ce travail est soutenu par des modèles, des simulations et des résultats qui focalisent sur l'importance de quantifier les techniques de préservation de la confidentialité et d'analyser le compromis entre la protection de la confidentialité et l'efficacité du partage du spectre
Radio frequencies, as currently allocated, are statically managed. Spectrum sharing between commercial users and incumbent users in the Federal bands has been considered by regulators, industry, and academia as a great way to enhance productivity and effectiveness in spectrum use. However, allowing secondary users to share frequency bands with sensitive government incumbent users creates new privacy threats in the form of inference attacks. Therefore, the aim of this thesis is to enhance the privacy of the incumbent while allowing secondary access to the spectrum. First, we present a brief description of different sharing regulations and privacy requirements in Federal bands. We also survey the privacy-preserving techniques (i.e., obfuscation) proposed in data mining and publishing to thwart inference attacks. Next, we propose and implement our approach to protect the operational frequency and location of the incumbent operations from inferences. We follow with research on frequency protection using inherent and explicit obfuscation to preserve the incumbent's privacy. Then, we address location protection using trust as the main countermeasure to identify and mitigate an inference risk. Finally, we present a risk-based framework that integrates our work and accommodates other privacy-preserving approaches. This work is supported with models, simulations and results that showcase our work and quantify the importance of evaluating privacy-preserving techniques and analyzing the trade-off between privacy protection and spectrum efficiency
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Hao, F., Geyong Min, M. Lin, C. Luo, and L. T. Yang. "MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks." 2014. http://hdl.handle.net/10454/10644.

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Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.
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Tseng, Shihhao, and 曾世豪. "Trust Inference and Social Network for Blog Recommendation ─ Physician Search of Virtual Community." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/78413194091907245283.

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碩士
輔仁大學
資訊管理學系
99
In recent years, social network sites ( SNS ) had developed rapidly. SNSs also can be viewed as an important information platform for knowledge establishing, knowledge sharing, and knowledge dissermation. However, some SNS platforms provide the query users to search for health information from the blogs on which the collectes the other users’ medical treatment’s experiences., Based on many blogs’ interactive relationships among the users, this research proposes a collaborative information and knowledge sharing. The practical experiences from the other users’ sharing can be as a reference for physicians choice, but the blogs’ contents are not easy to make sure their credibility. Therefore, this research develops a social network servcie aligning with trust mechanism for systematic collaborative decision-making model to achieve the goal of service innovation. This research proposes a new service model, service system development, and some experimental analysis and verification. According to the properties of SSME, the research motivation of physician search through virtual community can be a Web 2.0 applicaiton. Thus, a new service design of Web 2.0 SNS application is to develop systematic physician recommendation service system using SNA approach. When the query user search for the blogs, the analyized and recommended blogs can be determined by social computing and TidalTrust algorithm. However, the trackback is a interactive behavior hat can be explored the social relationships among the bloggers. The average shortest path length, clustering coefficient, and network centrality, the high level of citations and linkage relationships can be measurable to provide the search and recommendation results. In addition, the trust estimation can further facilitate the inference of reliable blogs’ recommendaitons. An integration of SNA and trust network can be used to design the service system that can provide the collaborative recommendation to enhance the decision making of query users. In ther experiments, the effects of the proposed service system using blogs’ search and recommendation.can be verified.
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Tsai, Cheng Han, and 蔡承翰. "A study of semantic web-based specialist recommendation & trust inference mechanism－a case of EMBA database." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/01081799265085803740.

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碩士
國立政治大學
資訊管理研究所
98
"Human Resource" is one of the most important assets of company, especially in knowledge-intensive industries. As network technologies developed, commercial job site has also become another kind of recruitment channel. But through this kind of channel, companies don’t have better chance to know new employee than traditional way. Therefore this study filters new employees by a Recommendation & Trust Inference mechanism. Hope that commercial job site would continue to keep the advantages of high efficiency in recruitment, and enhance its filtering capability at the same time. First, this study surveys literatures in recruitment channels. And it proposes a Recommendation & Trust Inference mechanism using a national university EMBA program member data as an example. The Recommendation mechanism recommend candidates having the same specialty by comparing their similarity of education and work experience. Furthermore, recruitment unit could use Trust Inference mechanism to get suitable candidates. Third, we conduct experiments to find the key parameters for the prototype system. Make the system able to work better and meet users’ needs. The prototype system combines the benefit of commercial job site which can quickly recruit a large number of employees and the feature providing more appropriate candidates for the company recommended by staff. Simultaneously by taking use of the FOAF format, we can unify the data format in online social network. The way mentioned above will effectively reduce the system set-up time.
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Books on the topic "Trust inference"

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Kawachi, Ichiro. Trust and Population Health. Edited by Eric M. Uslaner. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190274801.013.35.

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Research in public health approaches trust as a component of social cohesion, a characteristic of the social context in which an individual is embedded. This article discusses the theoretical mechanisms why living in a trusting environment might be associated with better health outcomes. A conceptual dilemma in health studies is that individual trust perceptions overlap with the personality trait of “cynical hostility” (from the field of psychology). Multi-level studies help to distinguish between the health effects of cynical distrust (an individual characteristic) and trustworthiness of the environment. I review the empirical studies linking trust and health outcomes. To date, trust has been examined as a contextual feature of residential neigborhoods and workplaces. Future research needs to strengthen causal inference.
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Dinesen, Peter Thisted, and René Bekkers. The Foundations of Individuals’ Generalized Social Trust. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190630782.003.0005.

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This chapter reviews the literature on the causes of individuals’ generalized social trust (trust in unknown others) from a wide range of social science disciplines. The chapter structures the review around two broad classes of explanations: dispositional explanations (trust as a disposition) and experiential explanations (trust as a response to individual experiences). Both have been examined in a number of related lines of research, which are reviewed and critically discussed in the chapter. Specific attention is paid to the potential for drawing causal inference—based on the nature of the data employed, and the methods used—in the studies reviewed.
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Ichino, Anna, and Greg Currie. Truth and Trust in Fiction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198805403.003.0004.

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This chapter examines two pathways through which fictions may affect beliefs: by invading readers’ cognitive system via heuristics and other sub-rational devices, and by expressing authorial beliefs that readers take to be reliable. Focusing mostly on the latter pathway, the chapter distinguishes fiction as a mechanism for the transmission of uncontroversial factual information from fiction as a means of expressing distinctive perspectives on evaluative propositions. In both cases, the inferences on which readers rely are precarious, and especially so with evaluative cases where there is little hope of independent verification. Moreover, trust, which in other contexts can increase the reliability of beliefs transmitted from person to person, cannot be much depended on when it comes to belief transmission from author to reader.
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Book chapters on the topic "Trust inference"

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Seyedi, Amir, Rachid Saadi, and Valérie Issarny. "Proximity-Based Trust Inference for Mobile Social Networking." In Trust Management V, 253–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22200-9_20.

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Cai, Liang, and Hao Chen. "On the Practicality of Motion Based Keystroke Inference Attack." In Trust and Trustworthy Computing, 273–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30921-2_16.

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Buckley, Oliver, Duncan Hodges, Melissa Hadgkiss, and Sarah Morris. "Keystroke Inference Using Smartphone Kinematics." In Human Aspects of Information Security, Privacy and Trust, 226–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58460-7_15.

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Alipour, Bizhan, Abdessamad Imine, and Michaël Rusinowitch. "Gender Inference for Facebook Picture Owners." In Trust, Privacy and Security in Digital Business, 145–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27813-7_10.

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Ziegler, Cai-Nicolas, and Jennifer Golbeck. "Models for Trust Inference in Social Networks." In Intelligent Systems Reference Library, 53–89. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15916-4_3.

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Yao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Subgraph Extraction for Trust Inference in Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1–15. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_357-1.

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Yao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Subgraph Extraction for Trust Inference in Social Networks." In Encyclopedia of Social Network Analysis and Mining, 2084–98. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_357.

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Yao, Yuan, Hanghang Tong, Feng Xu, and Jian Lu. "Subgraph Extraction for Trust Inference in Social Networks." In Encyclopedia of Social Network Analysis and Mining, 3011–25. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_357.

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Zhang, Huqiu, and Aad van Moorsel. "Evaluation of P2P Algorithms for Probabilistic Trust Inference in a Web of Trust." In Computer Performance Engineering, 242–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87412-6_18.

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Kim, Tiffany Hyun-Jin, Virgil Gligor, Jorge Guajardo, Jason Hong, and Adrian Perrig. "Soulmate or Acquaintance? Visualizing Tie Strength for Trust Inference." In Financial Cryptography and Data Security, 112–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41320-9_8.

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Conference papers on the topic "Trust inference"

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Liu, Guanfeng, Yan Wang, and Mehmet Orgun. "Trust Inference in Complex Trust-Oriented Social Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.248.

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Shi, Leyi, Yao Wang, and Xin Liu. "An ACO-Based Trust Inference Algorithm." In 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). IEEE, 2014. http://dx.doi.org/10.1109/bwcca.2014.70.

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Papaoikonomou, Athanasios, Magdalini Kardara, and Theodora Varvarigou. "Trust Inference in Online Social Networks." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2809418.

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He, Xu, Bin Liu, and Kejia Chen. "ITrace: An implicit trust inference method for trust-aware collaborative filtering." In ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS II: Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics (AMME 2018). Author(s), 2018. http://dx.doi.org/10.1063/1.5033766.

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Molina-Markham, Andres, and Joseph J. Rushanan. "Positioning, Navigation, and Timing Trust Inference Engine." In 2020 International Technical Meeting of The Institute of Navigation. Institute of Navigation, 2020. http://dx.doi.org/10.33012/2020.17195.

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Hamdi, Sana, Amel Bouzeghoub, Alda Lopes Gancarski, and Sadok Ben Yahia. "Trust Inference Computation for Online Social Networks." In 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, 2013. http://dx.doi.org/10.1109/trustcom.2013.240.

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Gupta, Shashank, Pulkit Parikh, Manish Gupta, and Vasudeva Varma. "Simultaneous Inference of User Representations and Trust." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110093.

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Chen, Hongwei, Zhiwei Ye, Wei Liu, and Chunzhi Wang. "Fuzzy Inference Trust in P2P Network Environment." In 2009 International Workshop on Intelligent Systems and Applications. IEEE, 2009. http://dx.doi.org/10.1109/iwisa.2009.5072876.

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O'Doherty, Daire, Salim Jouili, and Peter Van Roy. "Towards trust inference from bipartite social networks." In the 2nd ACM SIGMOD Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2304536.2304539.

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Hintersdorf, Dominik, Lukas Struppek, and Kristian Kersting. "To Trust or Not To Trust Prediction Scores for Membership Inference Attacks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/422.

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Membership inference attacks (MIAs) aim to determine whether a specific sample was used to train a predictive model. Knowing this may indeed lead to a privacy breach. Most MIAs, however, make use of the model's prediction scores - the probability of each output given some input - following the intuition that the trained model tends to behave differently on its training data. We argue that this is a fallacy for many modern deep network architectures. Consequently, MIAs will miserably fail since overconfidence leads to high false-positive rates not only on known domains but also on out-of-distribution data and implicitly acts as a defense against MIAs. Specifically, using generative adversarial networks, we are able to produce a potentially infinite number of samples falsely classified as part of the training data. In other words, the threat of MIAs is overestimated, and less information is leaked than previously assumed. Moreover, there is actually a trade-off between the overconfidence of models and their susceptibility to MIAs: the more classifiers know when they do not know, making low confidence predictions, the more they reveal the training data.
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Reports on the topic "Trust inference"

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Ding, Li, Pranam Kolari, Shashidhara Ganjugunte, Tim Finin, and Anupam Joshi. Modeling and Evaluating Trust Network Inference. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439712.

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Keefer, Philip, Sergio Perilla, and Razvan Vlaicu. Research Insights: Public Sector Employee Behavior and Attitudes during a Pandemic. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003388.

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New data on public sector employees from 18 Latin American countries shed light on the role of trust in the performance of government agencies. An original survey conducted during the first COVID-19 wave includes randomized experiments with pandemic-related treatments. Individual-level trust in coworkers, other public employees, and citizens is positively related to performance-enhancing behaviors and policy attitudes. High-trust and low-trust respondents report different assessments of their main work constraints. Also, they draw different inferences and prefer different policy responses when exposed to data-based framing treatments about social distancing outcomes in their countries.
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Vlaicu, Razvan. Trust, Collaboration, and Policy Attitudes in the Public Sector. Inter-American Development Bank, May 2021. http://dx.doi.org/10.18235/0003280.

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This paper examines new data on public sector employees from 18 Latin American countries to shed light on the role of trust in the performance of government agencies. We developed an original survey taken during the first COVID-19 wave that includes randomized experiments with pandemic-related treatments. We document that individual-level trust in coworkers, other public employees, and citizens is positively related to performance-enhancing behaviors, such as cooperation and information-sharing, and policy attitudes, such as openness to technological innovations in public service delivery. Trust is more strongly linked to positive behaviors and attitudes in non-merit-based civil service systems. High-trust and low-trust respondents report different assessments of their main work constraints. Also, they draw different inferences and prefer different policy responses when exposed to data-based framing treatments about social distancing outcomes in their countries. Low-trust public employees are more likely to assign responsibility for a negative outcome to the government and to prefer stricter enforcement of social distancing.
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