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Journal articles on the topic 'Trust inference'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Mandaglio, Domenico, and Andrea Tagarelli. "Generalized Preference Learning for Trust Network Inference." IEEE Access 7 (2019): 174583–96. http://dx.doi.org/10.1109/access.2019.2957191.

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12

Zhou, Peng, Xiapu Luo, and Rocky K. C. Chang. "Inference attacks against trust-based onion routing: Trust degree to the rescue." Computers & Security 39 (November 2013): 431–46. http://dx.doi.org/10.1016/j.cose.2013.09.007.

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13

Ruan, Yefeng, and Arjan Durresi. "A survey of trust management systems for online social communities – Trust modeling, trust inference and attacks." Knowledge-Based Systems 106 (August 2016): 150–63. http://dx.doi.org/10.1016/j.knosys.2016.05.042.

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14

Azadeh, Ali, Mansour Zarrin, Morteza Saberi, and Zohre Ebrahimi. "Improved Trust Prediction in Business Environments by Adaptive Neuro Fuzzy Inference Systems." International Journal of Advanced Research in Engineering 1, no. 1 (June 15, 2015): 22. http://dx.doi.org/10.24178/ijare.2015.1.1.22.

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Trust prediction turns out to be an important challenge when cooperation among intelligent agents with an impression of trust in their mind, is investigated. In other words, predicting trust values for future time slots help partners to identify the probability of continuing a relationship. Another important case to be considered is the context of trust, i.e. the services and business commitments for which a relationship is defined. Hence, intelligent agents should focus on improving trust to provide a stable and confident context. Modelling of trust between collaborating parties seems to be an important component of the business intelligence strategy. In this regard, a set of metrics have been considered by which the value of confidence level for predicted trust values has been estimated. These metrics are maturity, distance and density (MD2). Prediction of trust for future mutual relationships among agents is a problem that is addressed in this study. We introduce a simulation-based model which utilizes linguistic variables to create various scenarios. Then, future trust values among agents are predicted by the concept of adaptive neuro-fuzzy inference system (ANFIS). Mean absolute percentage errors (MAPEs) resulted from ANFIS are compared with confidence levels which are determined by applying MD2. Results determine the efficiency of MD2 for forecasting trust values. This is the first study that utilizes the concept of MD2 for improvement of business trust prediction.

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15

Sun, Hua, and Li Li. "A Trustable Approach Based on Fuzzy Inference for P2P Systems." Applied Mechanics and Materials 121-126 (October 2011): 3914–18. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.3914.

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Trust management is one of the key problems in the P2P systems and e-commerce. Before they have the transaction, people always want to know whether the other side of the transaction partners can be trusted and how much degree of their trustworthiness. This paper proposes a trustable approach based on fuzzy inference in P2P systems and gives a detail discussion of fuzzy inference. They can inference their trust against the trustworthiness of the rules and compute the trustworthiness of the conclusion. This approach is adapted to the P2P environment which the rule and the proof which may be showed as fuzzy, and the approach also have practical meaning for us to build an effective P2P system.

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16

S. "Current Trust Inference Mechanisms in Web Based Social Networks." Journal of Computer Science 8, no. 9 (September 1, 2012): 1496–500. http://dx.doi.org/10.3844/jcssp.2012.1496.1500.

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17

Liang, Zhengqiang, and Weisong Shi. "Analysis of ratings on trust inference in open environments." Performance Evaluation 65, no. 2 (February 2008): 99–128. http://dx.doi.org/10.1016/j.peva.2007.04.001.

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18

Yao, Yuan, Hanghang Tong, Xifeng Yan, Feng Xu, and Jian Lu. "Multi-Aspect + Transitivity + Bias: An Integral Trust Inference Model." IEEE Transactions on Knowledge and Data Engineering 26, no. 7 (July 2014): 1706–19. http://dx.doi.org/10.1109/tkde.2013.147.

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19

Halas, Matus. "In error we trust: an apology of abductive inference." Cambridge Review of International Affairs 28, no. 4 (February 13, 2015): 701–20. http://dx.doi.org/10.1080/09557571.2014.997193.

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20

Yao, Yuan, Feng Xu, YongLi Ren, HangHang Tong, and Jian Lü. "SelfTrust: leveraging self-assessment for trust inference in Internetware." Science China Information Sciences 56, no. 10 (October 2013): 1–14. http://dx.doi.org/10.1007/s11432-013-5005-4.

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21

Hosseinzadeh Aghdam, Mehdi, Morteza Analoui, and Peyman Kabiri. "Modelling trust networks using resistive circuits for trust-aware recommender systems." Journal of Information Science 43, no. 1 (July 10, 2016): 135–44. http://dx.doi.org/10.1177/0165551516628733.

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Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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22

ØRBÆK, P., and J. PALSBERG. "Trust in the λ-calculus." Journal of Functional Programming 7, no. 6 (November 1997): 557–91. http://dx.doi.org/10.1017/s0956796897002906.

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This paper introduces trust analysis for higher-order languages. Trust analysis encourages the programmer to make explicit the trustworthiness of data, and in return it can guarantee that no mistakes with respect to trust will be made at run-time. We present a confluent λ-calculus with explicit trust operations, and we equip it with a trust-type system which has the subject reduction property. Trust information is presented as annotations of the underlying Curry types, and type inference is computable in O(n3) time.

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23

Valencia-Martinez, Camilo Alejandro, Paulo Alonso Gaona-García, and Carlos Enrique Montenegro-Marin. "Design of a trust system for e-commerce platforms based on quality dimensions for linked open datasets." Journal of Information Systems Engineering and Management 8, no. 1 (January 3, 2023): 18756. http://dx.doi.org/10.55267/iadt.07.12741.

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This article describes a proposal about a trust system for e-commerce platform based on semantic web technologies and trust dimensions rules. We try to expose a system that allow to manage communication processes between e-commerce platforms and users in a trustworthy manner. It allows the data flows and transactions gain more trust across the entire process. All of this can be achieved through the inference of rules exposed in the defined ontology, complemented by a cloud-based system with microservices architecture. With the implementation of the system through an e-commerce platform, could consume data from the microservices in order to get inferences about its clients that want to buy or sell something within its system. This system was created based on rules defined by the ontology, as well as the microservices could be used to register information about multiple e-commerce transactions. The result of this work is the Ontology and semantic web rules defined and implemented through protege.

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24

Zahir, Yuan, and Moniz. "AgreeRelTrust—a Simple Implicit Trust Inference Model for Memory-Based Collaborative Filtering Recommendation Systems." Electronics 8, no. 4 (April 11, 2019): 427. http://dx.doi.org/10.3390/electronics8040427.

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Recommendation systems alleviate the problem of information overload by helping users find information relevant to their preference. Memory-based recommender systems use correlation-based similarity to measure the common interest among users. The trust between users is often used to address the issues associated with correlation-based similarity measures. However, in most applications, the trust relationships between users are not available. A popular method to extract the implicit trust relationship between users employs prediction accuracy. This method has several problems such as high computational cost and data sparsity. In this paper, addressing the problems associated with prediction accuracy-based trust extraction methods, we proposed a novel trust-based method called AgreeRelTrust. Unlike accuracy-based methods, this method does not require the calculation of initial prediction and the trust relationship is more meaningful. The collective agreements between any two users and their relative activities are fused to obtain the trust relationship. To evaluate the usefulness of our method, we applied it to three public data sets and compared the prediction accuracy with well-known collaborative filtering methods. The experimental results show our method has large improvements over the other methods.

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25

ASHTIANI, MEHRDAD, and MOHAMMAD ABDOLLAHI AZGOMI. "CONTEXTUALITY, INCOMPATIBILITY AND BIASED INFERENCE IN A QUANTUM-LIKE FORMULATION OF COMPUTATIONAL TRUST." Advances in Complex Systems 17, no. 05 (October 2014): 1450020. http://dx.doi.org/10.1142/s0219525914500209.

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Trust models play an important role in computational environments. One of the main aims of the work undertaken in this domain is to provide a model that can better describe the socio-technical nature of computational trust. It has been recently shown that quantum-like formulations in the field of human decision making can better explain the underlying nature of these types of processes. Based on this research, the aim of this paper is to propose a novel model of trust based on quantum probabilities as the underlying mathematics of quantum theory. It will be shown that by using this new mathematical framework, we will have a powerful mechanism to model the contextuality property of trust. Also, it is hypothesized that many events or evaluations in the context of trust can be and should be considered as incompatible, which is unique to the noncommutative structure of quantum probabilities. The main contribution of this paper will be that, by using the quantum Bayesian inference mechanism for belief updating in the framework of quantum theory, we propose a biased trust inference mechanism. This mechanism allows us to model the negative and positive biases that a trustor may subjectively feel toward a certain trustee candidate. It is shown that by using this bias, we can model and describe the exploration versus exploitation problem in the context of trust decision making, recency effects for recently good or bad transactions, filtering pessimistic and optimistic recommendations that may result in good-mouthing or bad-mouthing attacks, the attitude of the trustor toward risk and uncertainty in different situations and the pseudo-transitivity property of trust. Finally, we have conducted several experimental evaluations in order to demonstrate the effectiveness of the proposed model in different scenarios.

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26

WeiFeng Tung, ShihHao, and Tseng. "A Hybrid Approach of Trust Inference and Expert Social Search." Journal of Convergence Information Technology 7, no. 7 (April 30, 2012): 342–51. http://dx.doi.org/10.4156/jcit.vol7.issue7.40.

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27

Zhou, Peng, Xiaojing Gu, Jie Zhang, and Minrui Fei. "A priori trust inference with context-aware stereotypical deep learning." Knowledge-Based Systems 88 (November 2015): 97–106. http://dx.doi.org/10.1016/j.knosys.2015.08.003.

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28

Xia, Hui, Zhetao Li, Yuhui Zheng, Anfeng Liu, Young-June Choi, and Hiroo Sekiya. "A Novel Light-Weight Subjective Trust Inference Framework in MANETs." IEEE Transactions on Sustainable Computing 5, no. 2 (April 1, 2020): 236–48. http://dx.doi.org/10.1109/tsusc.2018.2817219.

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29

Ravichandran M, Subramanian K M, and Jothikumar R. "An Enhanced and Efficient Multi-View Clustering Trust Inference Approach by GA Model." International Journal of Information Technology and Web Engineering 14, no. 4 (October 2019): 64–78. http://dx.doi.org/10.4018/ijitwe.2019100104.

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Multi-view affinity propagation (MAP) methods are widely accepted techniques, measure the within-view clustering and clustering consistency. These suffer from similarity and correlation between clusters. The trust and similarity measured was introduced as a new approach to overcome the problem. But these approaches suffer from low accuracy and coverage due to avoidance of implicit trust. So, a framework called multi-view clustering based on gray affinity (MVC-GA) created by integrating both similarity and implicit trust. Similarity between two clusters is obtained by applying the Pearson Correlation Coefficient-based similarity. It utilizes the collaborative filter-based trust evaluation for each clustered view in terms of the similarity based on the gray affinity nn algorithm. Classification of incomplete occurrences is addressed based on GA Function. Experiments on the benchmark data sets have been performed to validate the proposed framework. It is shown that MVC-GA can improve the multi-view clustering accuracy and coverage.

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30

Gao, Yan, and Wenfen Liu. "BeTrust: A Dynamic Trust Model Based on Bayesian Inference and Tsallis Entropy for Medical Sensor Networks." Journal of Sensors 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/649392.

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With the rapid development and application of medical sensor networks, the security has become a big challenge to be resolved. Trust mechanism as a method of “soft security” has been proposed to guarantee the network security. Trust models to compute the trustworthiness of single node and each path are constructed, respectively, in this paper. For the trust relationship between nodes, trust value in every interval is quantified based on Bayesian inference. A node estimates the parameters of prior distribution by using the collected recommendation information and obtains the posterior distribution combined with direct interactions. Further, the weights of trust values are allocated through using the ordered weighted vector twice and overall trust degree is represented. With the associated properties of Tsallis entropy, the definition of path Tsallis entropy is put forward, which can comprehensively measure the uncertainty of each path. Then a method to calculate the credibility of each path is derived. The simulation results show that the proposed models can correctly reflect the dynamic of node behavior, quickly identify the malicious attacks, and effectively avoid such path containing low-trust nodes so as to enhance the robustness.

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31

Cardoso, Rui Costa, Abel J. P. Gomes, and Mario M. Freire. "A User Trust System for Online Games—Part II: A Subjective Logic Approach for Trust Inference." IEEE Transactions on Computational Intelligence and AI in Games 9, no. 4 (December 2017): 354–68. http://dx.doi.org/10.1109/tciaig.2016.2593000.

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32

Wang, Yan, Lei Li, and Guanfeng Liu. "Social context-aware trust inference for trust enhancement in social network based recommendations on service providers." World Wide Web 18, no. 1 (August 23, 2013): 159–84. http://dx.doi.org/10.1007/s11280-013-0241-5.

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33

Zhou, Jiyuan, Xing Jin, Lanping Yu, Limin Xue, and Yizhi Ren. "TruthTrust: Truth Inference-Based Trust Management Mechanism on a Crowdsourcing Platform." Sensors 21, no. 8 (April 7, 2021): 2578. http://dx.doi.org/10.3390/s21082578.

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On a crowdsourcing platform, in order to cheat for rewards or sabotage the crowdsourcing processes, spam workers may submit numerous erroneous answers to the tasks published by requesters. This type of behavior extremely reduces the completion rate of tasks and the enthusiasm of honest users, which may lead a crowdsourcing platform to a failure. Defending against malicious attacks is an important issue in crowdsourcing, which has been extensively addressed by existing methods, e.g., verification-based defense mechanisms, data analysis solutions, trust-based defense models, and workers’ properties matching mechanisms. However, verification-based defense mechanisms will consume a lot of resources, and data analysis solutions cannot motivate workers to provide high-quality services. Trust-based defense models and workers’ properties matching mechanisms cannot guarantee the authenticity of information when collusion requesters publish shadow tasks to help malicious workers get more participation opportunities. To defend such collusion attacks in crowdsourcing platforms, we propose a new defense model named TruthTrust. Firstly, we define a complete life cycle system that from users’ interaction to workers’ recommendation, and separately define the trust value of each worker and the credence of each requester. Secondly, in order to ensure the authenticity of the information, we establish a trust model based on the CRH framework. The calculated truth value and weight are used to define the global properties of workers and requesters. Moreover, we propose a reverse mechanism to improve the resistance under attacks. Finally, extensive experiments demonstrate that TruthTrust significantly outperforms the state-of-the-art approaches in terms of effective task completion rate.

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34

Gupta, Shikha, and Anil K. Saini. "Modeling Trust Based Risk Management in Cloud Adoption using Fuzzy Inference System." International Journal of Technology Diffusion 8, no. 1 (January 2017): 52–60. http://dx.doi.org/10.4018/ijtd.2017010104.

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Cloud has revolutionized the entire landscape of IT usage, storage and maintenance. It has shifted the focus from owning physical IT infrastructure, facility and storage to the use of same in an abstract form with pay per use facility. This has led to economizing the entire IT infrastructure. Cloud's various characteristics including on demand pay per use, scalability and flexibility of computing highly depends on cloud service provider which can often lead to low results and serious issues. These issues may include concerns about losses due to non-compliance with the promises made. Hence selection of cloud service provider can play a positive or negative role in establishing an initial trust between the cloud service client and provider. The authors propose a model of Trust based Risk management of cloud adoption which can be used by cloud users. The model provides the functioning of comparing service providers using calculated trust ratings.

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35

MA, Yao, Hongwei LU, and Zaobin GAN. "Discovery of the Optimal Trust Inference Path for Online Social Networks." IEICE Transactions on Information and Systems E97.D, no. 4 (2014): 673–84. http://dx.doi.org/10.1587/transinf.e97.d.673.

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36

Hao, Fei, Geyong Min, Man Lin, Changqing Luo, and Laurence T. Yang. "MobiFuzzyTrust: An Efficient Fuzzy Trust Inference Mechanism in Mobile Social Networks." IEEE Transactions on Parallel and Distributed Systems 25, no. 11 (November 2014): 2944–55. http://dx.doi.org/10.1109/tpds.2013.309.

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37

Zhang, Ke, and Xiaoxue Chen. "Herding in a P2P lending market: Rational inference OR irrational trust?" Electronic Commerce Research and Applications 23 (May 2017): 45–53. http://dx.doi.org/10.1016/j.elerap.2017.04.001.

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38

Fu, Weiming, Jiahu Qin, Qing Ling, Yu Kang, and Baijia Ye. "Trust-Region Based Stochastic Variational Inference for Distributed and Asynchronous Networks." Journal of Systems Science and Complexity 35, no. 6 (December 2022): 2062–76. http://dx.doi.org/10.1007/s11424-022-2085-5.

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39

Liu, Zhenpeng, Ruilin Li, Dewei Miao, Lele Ren, and Yonggang Zhao. "Membership Inference Defense in Distributed Federated Learning Based on Gradient Differential Privacy and Trust Domain Division Mechanisms." Security and Communication Networks 2022 (July 14, 2022): 1–14. http://dx.doi.org/10.1155/2022/1615476.

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Distributed federated learning models are vulnerable to membership inference attacks (MIA) because they remember information about their training data. Through a comprehensive privacy analysis of distributed federated learning models, we design an attack model based on generative adversarial networks (GAN) and member inference attacks (MIA). Malicious participants (attackers) utilize the attack model to successfully reconstruct training sets of other regular participants without any negative impact on the global model. To solve this problem, we apply the differential privacy method to the training process of the model, which effectively reduces the accuracy of member inference attacks by clipping the gradient and adding noise to it. In addition, we manage the participants hierarchically through the method of trust domain division to alleviate the performance degradation of the model caused by differential privacy processing. Experimental results show that in distributed federated learning, our designed scheme can effectively defend against member inference attacks in white-box scenarios and maintain the usability of the global model, realizing an effective trade-off between privacy and usability.

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40

Howah, Kenneth, and Ritesh Chugh. "Do We Trust the Internet?" Journal of Global Information Management 27, no. 3 (July 2019): 87–100. http://dx.doi.org/10.4018/jgim.2019070105.

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The threat profile of spyware is increasing rapidly when we consider the pervasiveness of computing in everyday life. The surreptitious nature of spyware can lead to information mining, data theft and exploitation of stolen data. This article aims to explore the relationship between trust in the Internet and neglecting risks in downloading and installing free potentially spyware-infected software. This study found that trust can be viewed as an indirect function of ignorance and overconfidence through the mechanism of the calculated probability of risk. An enhanced model of trust, based on Li and Betts' trust model has been proposed with two additional vectors. The inference drawn from the study is that in most users' minds, the calculated or perceived risk is substantially less than the anticipated benefit of the software. The analysis shows that users trust the Internet when it comes to downloading and installing potentially spyware-infected software, although such nonchalant trust appears to be based on ignorance and over-confidence.

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41

Mahendrakar Shaymrao, Swetha, Pushpa Sothenahalli Krishnaraju, Thungamani Mahalingappa, and Manjunath Thimmasandra Narayanappa. "Design and development of anonymous location based routing for mobile ad-hoc network." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 2743. http://dx.doi.org/10.11591/ijece.v12i3.pp2743-2755.

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Mobile ad-hoc network (MANET) consists of wireless nodes interacting with each other impulsively over the air. MANET network is dynamic in nature because of which there is high risk in security. In MANET keeping node and routing secure is main task. Many proposed methods have tried to clear this issue but unable to fully resolve. The proposed method has strong secure anonymous location based routing (S2ALBR) method for MANET using optimal partitioning and trust inference model. Here initially partitions of network is done into sectors by using optimal tug of war (OTW) algorithm and compute the trustiness of every node by parameters received signal strength, mobility, path loss and co-operation rate. The process of trust computation is optimized by the optimal decided trust inference (ODTI) model, which provides the trustiness of each node, highest trust owned node is done in each sector and intermediate nodes used for transmission. The proposed method is focusing towards optimization with respect to parameter such as energy, delay, network lifetime, and throughput also above parameter is compared with the existing methods like anonymous location-based efficient routing protocol (ALERT), anonymous location-aided routing in suspicious MANET (ALARM) and authenticated anonymous secure routing (AASR).

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42

Bati, Ghassan F., and Vivek K. Singh. "NADAL: A Neighbor-Aware Deep Learning Approach for Inferring Interpersonal Trust Using Smartphone Data." Computers 10, no. 1 (December 24, 2020): 3. http://dx.doi.org/10.3390/computers10010003.

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Interpersonal trust mediates multiple socio-technical systems and has implications for personal and societal well-being. Consequently, it is crucial to devise novel machine learning methods to infer interpersonal trust automatically using mobile sensor-based behavioral data. Considering that social relationships are often affected by neighboring relationships within the same network, this work proposes using a novel neighbor-aware deep learning architecture (NADAL) to enhance the inference of interpersonal trust scores. Based on analysis of call, SMS, and Bluetooth interaction data from a one-year field study involving 130 participants, we report that: (1) adding information about neighboring relationships improves trust score prediction in both shallow and deep learning approaches; and (2) a custom-designed neighbor-aware deep learning architecture outperforms a baseline feature concatenation based deep learning approach. The results obtained at interpersonal trust prediction are promising and have multiple implications for trust-aware applications in the emerging social internet of things.

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Mao, Yuqing, and Haifeng Shen. "Web of Credit: Adaptive Personalized Trust Network Inference From Online Rating Data." IEEE Transactions on Computational Social Systems 3, no. 4 (December 2016): 176–89. http://dx.doi.org/10.1109/tcss.2016.2639016.

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Abdullah, Lazim, Noor Maizura Mohamad Noor, and Wan Abd Aziz Wan Mohd Amin. "Unit Trust Forecasting using Adaptive Neural Fuzzy Inference System: A Performance Comparison." Procedia - Social and Behavioral Sciences 57 (October 2012): 132–39. http://dx.doi.org/10.1016/j.sbspro.2012.09.1166.

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Pal, Bithika, and Mamata Jenamani. "Trust inference using implicit influence and projected user network for item recommendation." Journal of Intelligent Information Systems 52, no. 2 (November 8, 2018): 425–50. http://dx.doi.org/10.1007/s10844-018-0537-0.

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Kuter, Ugur, and Jennifer Golbeck. "Using probabilistic confidence models for trust inference in Web-based social networks." ACM Transactions on Internet Technology 10, no. 2 (May 2010): 1–23. http://dx.doi.org/10.1145/1754393.1754397.

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Chatterjee, Shoutir Kishore. "Some Thoughts on the Foundations of Statistical Inference 1." Calcutta Statistical Association Bulletin 38, no. 1-2 (March 1989): 1–26. http://dx.doi.org/10.1177/0008068319890101.

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I am fully conscious of the honour that you have bestowed upon me by electing me President of the Section of Statistics for this 76-th session of Indian Science Congress. I know that this office carries with it an onerous responsibility and illustrious predecessors have set very high standards for it. I would strive my best to measure up to your trust and to make this session a success. I would need the good wishes and cooperation of all of you in this effort.

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Felkner, Anna. "Two Extensions of Trust Management Languages." Journal of Telecommunications and Information Technology 1 (March 31, 2020): 87–94. http://dx.doi.org/10.26636/jtit.2020.138719.

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This article is focused on the family of role-based trust management languages (RT). Trust management languages are a useful method of representing security credentials and policies in large distributed access control mechanisms. They provide sets of credentials that are assigned to individual roles performed by the speciﬁc entities. These credentials provide relevant information about security policies issued by trusted authorities and deﬁne user permissions. RT languages describe the individual entities and the roles that these entities play in a given environment. A set of credentials representing a given security policy deﬁnes which entity has the necessary rights to access a speciﬁc resource and which entity does not have such rights. This study presents the results of research focusing on the potential of the family of RT languages. Its purpose is to show how security policies may be applied more widely by applying an inference system, and then using the extensions of the credentials, by taking into account time-related information or the conditions imposed with regard to the validity of such credentials. Each of these extensions can be used jointly or separately, oﬀering even a wider range of opportunities

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Dziubiński, Zbigniew, and Martyna Sitek. "SOCIAL TRUST AND TOURISM." Folia Turistica 49 (December 31, 2018): 225–41. http://dx.doi.org/10.5604/01.3001.0013.0818.

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Purpose. The aim of the work is to learn the theoretical assumptions regarding the culture of trust and culture of distrust in relation to (post) modern tourism. Method. The basic method is to analyseliterature on the subject using the two theoretical perspectives dominating in sociology, namely the perspectives of functionalism and the perspective of symbolic interactionism. Results. If there is a high level of trust in the field of tourism, the result of this situation is optimism, commitment and innovation, a climate of mobilisation, activity, the potential for creativity is released, and people have a sense of freedom and subjectivity. As a consequence, structural conditions for the development of tourism are created. The reverse situation occurs when the culture of mistrust is dominant in tourism, which leads to withdrawal, passivity, demobilisation, atomisation, alienation and distancing itself from other individual and collective actors of the tourist scene. This state of affairs causes reduction in the subjective potential of the tourism community, the ability to creatively, innovatively and strengthen the culture of distrust in tourism. Research and conclusions limitations. No limitations of the tests occurred. Inference, as in the social sciences, is probabilistic in nature. Practical implications. The final conclusions should contribute to greater awareness of tourism participants about the role and importance of social trust in the development of the area of life that interests us. Originality. The description of tourism uses a soft variable which is social trust. The work is to make all tourism participants aware that this variable, after aggregation, is transformed into a powerful force that determines the progress or regression of tourism. Type of work. The article is a theoretical review in which the analysis of the phenomenon of modern tourism from a sociological perspective and using the notion of the innovative concept in scientific circulation of social trust is undertaken.

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Li, Jianan, Jun Wu, Jianhua Li, Ali Kashif Bashir, Md Jalil Piran, and Ashiq Anjum. "Blockchain-Based Trust Edge Knowledge Inference of Multi-Robot Systems for Collaborative Tasks." IEEE Communications Magazine 59, no. 7 (July 2021): 94–100. http://dx.doi.org/10.1109/mcom.001.2000419.

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