Trust Decay Research Articles

Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments.

A cooperative detection scheme for malicious nodes based on D-S trust evidence reasoning in mobile crowdsensing networks

Mobile crowdsensing possesses excellent collaborative capabilities and has become an important solution for addressing complex sensing tasks. However, the high mobility and anonymity of nodes bring security risks such as forging false data and maliciously consuming platform resources. To address these problems, a cooperative detection scheme for malicious nodes based on Dempster-Shafer(D-S) trust evidence reasoning is proposed. Firstly, the sensing platform collects trust data of nodes through a trust evidence chain, which includes data credibility, recommendation trustworthiness, task cooperativeness and behavioral credibility. Meanwhile, trust decay and control mechanisms are adopted to dynamically update the trust evidence chain. Additionally, a recommendation trust network based on recommendation value is constructed through the collaboration between platform and nodes. The Isolation Forest is used to analyze node behavior, and the behavioral credibility is calculated using Bayes’ rule. Then, K-Means, Gaussian Mixture Model (GMM), Three-way Decision and Grey Cluster are used to classify nodes into trusted, suspicious and malicious sets. Finally, the purification of suspicious set is achieved through raft consensus among trusted nodes. Simulation tests demonstrate the performance of the proposed scheme in accuracy, false negative rate, false positive rate and time consumption. Overall, the scheme has better detection performance when using GMM.

Improving consensus in social network group decision-making: Emphasizing overlapping subgroups and interactive behaviors

The multilevel nature of social networks and the multiple identities of decision makers (DMs) have led to the co-existence of overlapping and non-overlapping subgroups in social network group decision-making. This paper focuses on three issues in an intuitionistic fuzzy environment in this situation. Firstly, some unknown trust relationships exist between DMs belonging to different subgroups. Secondly, interactions between DMs and subgroups affect the final consensus result. Thirdly, opinions of DMs belonging to multiple subgroups simultaneously (i.e., key DMs) affect the opinions of these overlapping subgroups to varying degrees. To address the first issue, a trust propagation operator considering trust influence and trust decay is proposed to estimate the trust values between DMs belonging to different subgroups. To solve the second issue, we describe interactions within and between subgroups by factors such as trust value and overlapping level. To manage the third issue, we introduce the Choquet integral to gather opinions and construct an interactive behavior-driven hierarchical consensus model to obtain the group opinion for the non-additive interactions between DMs and subgroups. Finally, the effectiveness and applicability of the method are verified through illustrative examples and comparisons.

A Minimum Adjustment Cost Consensus Framework Considering Harmony Degrees and Trust Propagation for Social Network Group Decision Making

In consensus-based social network group decision-making (SN-GDM) problems, the low degree of harmony caused by opinion discrepancies and weak trust relationships may lead to high consensus costs and conflict among experts. To address these problems, we design a minimum adjustment cost consensus framework considering harmony degrees in SN-GDM with incomplete 2-tuple linguistic trust. First, considering the decay of trust through its propagation process, a discount-factor-based 2-tuple linguistic trust propagation operator is proposed to estimate the unknown trust relationships among experts. Then, the harmony degrees are measured based on opinion discrepancies and 2-tuple linguistic trust values, and experts’ weights could be obtained. To improve the consensus levels of the inconsistent experts, a minimum adjustment cost consensus model considering harmony degrees is explored to generate interval recommendations. The existence of optimal solutions and the convergence of the proposed consensus model are also proved. Finally, the validity of the proposed consensus framework is verified by the case of base station selection. With the higher overall harmony degree and individual consensus level, we extend the classical minimum cost consensus model and incorporate the harmony degrees among experts into the consensus reaching process.

A consensus model for group decision-making with personalized individual self-confidence and trust semantics: A perspective on dynamic social network interactions

Natural language could be a common method for individuals to specific social relationships or self-evaluation (i.e., assured or unconfident). Hence, it is important to contemplate customized linguistics in collective decisions that involve linguistic variables, because the same linguistic term set may have totally different individual numerical scales. This research develops a consensus model taking into consideration the personalized individual self-confidence and trust semantics in dynamic social network group decision-making scenarios. Firstly, an optimization model driven by best additive consistency is proposed to derive the personalized self-confidence and trust semantics of individuals. Subsequently, a trust aggregation technique supporting the shortest path is outlined for weight distribution. The proposed technique not only considers the personalized individual trust linguistics but can also effectively decrease the risk of information distortion caused by trust decay. Moreover, a consensus feedback strategy is developed that combines opinion adjustment and dynamic trust interaction to enhance the potency of consensus. Totally different from most existing consensus models supported static trust network, the bestowed consensus feedback mechanism considers the inverse driving result of opinion adjustment on trust interaction within the consensus reaching processes. Finally, numerical examples and comparisons are provided to justify the practicability of the proposed model.

Policing the pandemic in Australia and New Zealand: lessons for trust and legitimacy

PurposeThis paper aims to assess COVID-19 as presenting both a crisis and opportunity for police trust and legitimacy by considering the role of police in delivering the legislative requirements of government and enforcing various health orders across Australia and New Zealand.Design/methodology/approachThe research relies on a mixed-methods analysis of national, commonwealth, state and territory policy, corporate police reports, academic commentary and media coverage throughout the pandemic. Survey data gathered during the pandemic relevant to trust and legitimacy in police and government is also analysed.FindingsFive findings relating to police trust and legitimacy are identified. They reveal that police mostly did seize the pandemic as an opportunity to implement practices that enhanced perceptions of trust and legitimacy. However, even where police were able to leverage COVID-19 as an opportunity, the protracted nature of the pandemic posed a challenge for maintaining trust and legitimacy gains. The findings also underscore the importance of a continued focus on building trust and legitimacy post-pandemic to counter any lingering consequences.Research limitations/implicationsThe applicability of the findings outside the Australian and New Zealand context may be limited, given differences in jurisdictional legislative frameworks and policing operational environments.Practical implicationsThis study identifies good community engagement practice for pandemic policing, contributes to communication strategies for managing trust decay during an emergency, forecasts ongoing trust and legitimacy challenges to policing’s post-pandemic operational environment and enhances aspects of post-pandemic recruitment approaches.Originality/valueThe findings contribute to emerging police practice and research on building and sustaining trust and legitimacy during periods of uncertainty and volatility, such as during and after a pandemic.

Trust decay in the American healthcare delivery system

Trust decay in the American healthcare delivery system

Trust decay in the American healthcare delivery system

This treatise examines the four main principles of biomedical ethics, i.e., beneficence, nonmaleficence, autonomy, and justice, and their impact on the healthcare system and providers legally permitted to prescribe or recommend medicines in the United States. It defines the four principles of biomedical ethics and describes how failure to achieve them has contributed to trust decay in the United States healthcare delivery system over the last several decades, that is to say, medical mistrust. An illustrative case of conflicts will be presented that provides insight into whether protocols practiced by the healthcare delivery system to treat intractable pain conform to the principles of biomedical ethics. The increasing distrust in the American healthcare delivery system of two disparate minority populations will be examined. An illustrative case study of a disabled patient’s experiences with the standardized healthcare delivery system provides insight into healthcare delivery system administratively mandated policies that may violate the principles of biomedical ethics resulting in established medical protocols that require disabled intractable pain patients to succumb to an addiction disorder.

Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks

Predicting trust is a classic problem in social networks analysis. Furthermore, while most early approaches ignore distrust, recent works seem to consider it as important, if not more important, than trust itself. In this paper, we present a novel approach to predict both trust and distrust in Weighted Signed Social Networks very efficiently and in a satisfyingly accurate and robust way. Therefore allowing people to have healthier online presence and interactions.Being a local metric that does not rely on trust propagation, the proposed approach does not suffer from some serious limitations like trust decay, opinions conflict, path dependence, and time complexity. Moreover, our experiments on four real-world datasets show that, in addition to its simplicity and extensibility, this algorithm is robust to network sparsity, and provides satisfyingly accurate and very fast predictions.

CTRUST: A Dynamic Trust Model for Collaborative Applications in the Internet of Things

Security through trust presents a viable solution for threat management in the Internet of Things (IoT). Currently, a well-defined trust management framework for collaborative applications on the IoT platform does not exist. In order to estimate reliably the trust values of nodes within a system, the trust should be measured by suitable parameters that are based on the nodes’ functional properties in the application context. Existing models do not clearly outline the parametrization of trust. Also, trust decay is inadequately modeled in most current models. In addition, trust recommendations are usually inaccurately weighted with respect to previous trust, thereby increasing the effect of bad recommendations. A new model, CTRUST, is proposed to resolve these shortcomings. In CTRUST, trust is accurately parametrized while recommendations are evaluated through belief functions. The effects of trust decay and maturity on the trust evaluation process were studied. Each trust component is neatly modeled by appropriate mathematical functions. CTRUST was implemented in a collaborative download application and its performance was evaluated based on the utility derived and its trust accuracy, convergence, and resiliency. The results indicate that IoT collaborative applications based on CTRUST gain a significant improvement in performance, in terms of efficiency and security.

The ecology of trust among hackers

Malicious hackers profit from the division of labour among highly skilled associates. However, duplicity and betrayal form an intrinsic part of their daily operations. This article examines how a community of hackers uses an automated reputation system to enhance trust among its members. We analyse 449,478 feedbacks collected over 27 months that rate the trustworthiness of 29,985 individuals belonging to the largest computer hacking forum. Only a tiny fraction of the forum membership (2.4%) participates in the vast majority (75%) of ‘trust exchanges’, limiting its utility. We observe a reporting bias where the propensity to report positive outcomes is 2.81 times greater among beginner hackers than among forum administrators. Reputation systems do not protect against trust decay caused here by the rapid expansion of the community. Finally, a qualitative analysis of 25,000 randomly selected feedbacks indicates that a diverse set of behaviours, skills and attitudes trigger assessments of trustworthiness.

Trust Evaluation in Online Social Networks Using Generalized Network Flow

In online social networks (OSNs), to evaluate trust from one user to another indirectly connected user, the trust evidence in the trusted paths (i.e., paths built through intermediate trustful users) should be carefully treated. Some paths may overlap with each other, leading to a unique challenge of path dependence , i.e., how to aggregate the trust values of multiple dependent trusted paths. OSNs bear the characteristic of high clustering, which makes the path dependence phenomenon common. Another challenge is trust decay through propagation, i.e., how to propagate trust along a trusted path, considering the possible decay in each node. We analyze the similarity between trust propagation and network flow, and convert a trust evaluation task with path dependence and trust decay into a generalized network flow problem. We propose a modified flow-based trust evaluation scheme GFTrust , in which we address path dependence using network flow, and model trust decay with the leakage associated with each node. Experimental results, with the real social network data sets of Epinions and Advogato, demonstrate that GFTrust can predict trust in OSNs with a high accuracy, and verify its preferable properties.

Research on Trust Evaluation Access Control Model Based on Dynamic Bayesian Network in Trusted Domain

Aiming at solving the difficulties of interaction synergism and insecurity of the multi-agent system, a trusted evaluation access control model based on dynamic Bayesian network is proposed in this paper. The model establishes trust domains according to the interaction records of agent itself and the history. The influences of time and interactive behavior on trust evaluation are also considered. Historical interactive records, trust decay control factor and punish factor etc. are introduced. And the sensitivity and accuracy of dynamic interactions are thus improved as well as the safety of the access process is enhanced.

Information diffusion in distributed OSN: The impact of trusted relationships

Distributed Online Social Networks (DOSN) are a valid alternative to OSN based on peer-to-peer communications. Without centralised data management, DOSN must provide the users with higher level of control over their personal information and privacy. Thus, users may wish to restrict their personal network to a limited set of peers, depending on the level of trust with them. This means that the effective social network (used for information exchange) may be a subset of the complete social network, and may present different structural patterns, which could limit information diffusion. In this paper, we estimate the capability of DOSN to diffuse content based on trust between social peers. To have a realistic representation of a OSN friendship graph, we consider a large-scale Facebook network, from which we estimate the trust level between friends. Then, we consider only social links above a certain threshold of trust, and we analyse the potential capability of the resulting graph to spread information through several structural indices. We test four possible thresholds, coinciding with the definition of personal social circles derived from sociology and anthropology. The results show that limiting the network to “active social contacts” leads to a graph with high network connectivity, where the nodes are still well-connected to each other, thus information can potentially cover a large number of nodes with respect to the original graph. On the other hand, the coverage drops for more restrictive assumptions. Nevertheless the re-insertion of a single excluded friend for each user is sufficient to obtain good coverage (i.e., always higher than 40 %) even in the most restricted graphs. We also analyse the potential capability of the network to spread information (i.e., network spreadability), studying the properties of the social paths between any pairs of users in the graph, which represent the effective channels traversed by information. The value of contact frequency between pairs of users determines a decay of trust along the path (the higher the contact frequency the lower the decay), and a consequent decay in the level of trustworthiness of information traversing the path. We show that selecting the link to re-insert in the network with probability proportional to its level of trust is the best re-insertion strategy, as it leads to the best connectivity/spreadability combination.

A study on impact of untrustworthy and older recommendations over T-Grid computational model

Trust can be either direct or indirect based on the direct impression established by the recipient of the services or feedback given by recommenders over the service provider respectively. However, untrustworthy recommendations may lead to misuse of valuable resources, especially in grid like distributed environment where continuous dynamic sharing of the resources is involved. Further, a genuine recommendation may become older over a period of time in a trust database and using such values in trust evaluation may lead to misinterpreted estimates of the trust. Hence, eliminating such untrustworthy and older recommendations from the interactions can construct a robust and reliable grid computing environment. Proposed T-Grid model eliminates untrustworthy recommendations from the grid while considering parameters like credibility and trust decay of a recommending entity. This paper analyzes the performance evaluation of proposed T-Grid model in terms of trustworthiness of the resource providers, effect of decay and utilization ratio of the resources. Results of the analysis are produced.

Uncertain News

Trust influences a range of human behaviors including health decision making. Over the past 60 years a significant industry has sprung up to influence public opinion and mobilize grassroots challenges against evidence-based threats to vested interests. Simultaneously, media reports of scientific fraud, misrepresentation, constantly changing “evidence” for health, and “hyped” predictions of disasters that were ultimately less significant amplify doubts about the reliability of scientific evidence and technology when hazards arise. This has contributed to the appearance of decay of trust in the veracity of scientific claims. Population responses during communicable disease epidemics illustrate these interacting processes that simultaneously create uncertainty and significant discomfort within communities. Research on the relative influences of formal versus informal information sources in driving protective behavior during recent influenza epidemics shows how both the uptake of everyday preventive practices such as hand hygiene, and specific health interventions, such as vaccination are affected by these processes. We review recent work on influenza-related personal preventive practices, with a particular focus on the shifting roles and utility of formal and informal sources in decision making among the public, and consider the implications within the context of prevailing levels of trust, uncertainty, and doubt surrounding health care recommendations.

Trust in Science

Trust in science means in effect trust in scholars and their actions. Both the strategic trust based on concrete estimates of trustworthiness and the culture of trust understood as a general imperative to be trustful based on the general presumption of trustworthiness are engendered by the axiological and normative framework typical for the domain of science and known as a scientific ethos. The classical codification of the ethos of science was proposed by Robert K. Merton by means of four principles: universalism, communalism, disinterestedness and organized scepticism. Each of them is shown to facilitate or even evoke the truthful, competent, sincere and honest — in brief: trustworthy — conduct of scholars. The ethos of science and its implication — the trustworthiness of scholars — explain relatively low levels of fraud and plagiarism as compared with other domains. Unfortunately this description is adequate only with respect to the traditional model of `academic science'. In our time we have witnessed the emergence of a different model of science characterized by dependence on huge financial resources, privatization and secrecy of research, commodification of research results, bureaucratization of scientific institutions and instrumentalization of science by subjecting it to extra-scientific interests. In this period of `post-academic science' Mertonian norms lose some of their binding moral power, and the decay of trust in science is the predictable result. Consequently, the opportunities for, and actual cases of, fraud and plagiarism seem on the increase. To oppose this tendency it is necessary to rejuvenate the ethos of science by returning to Mertonian principles, but at the same time reformulating them in ways more adequate to the current institutional structures of post-academic science.

Cycles of trust and distrust in joint-ventures

A joint-venture is one of a number of possible cooperative strategies that an organization may pursue and may be constrasted to competitive strategies that are more usually referred to in the literature on business and management. This article is based upon an international study of joint-ventures and develops the notion that trust presents a useful core concept for understanding the performance of joint-ventures. In order to do this, a quite formalized model is developed in which joint-venture performance is seen as an outcome of contextual variables with trust acting as an intermediate variable. Key variables are seen to be: the pattern of interdependencies between the parent companies and the joint-venture, the degree of competition endogenous to the joint-venture, and the ambiguity of the joint-venture's activities and objectives. The key aspect of trust that is developed concerns the extent to which an organizational form provides reliable knowledge about future outcomes with two dimensions of trust identified as of special significance. First, is personal trust which is based upon the perceived reliability of the individuals to fulfil expectations and is seen to be an outcome of the nature of social networks to which people belong. Second, is procedural or impersonal trust which is based upon the perceived reliability of formalized systems and computations for making reliable decisions, and is seen to be an outcome of the rules and industrial recipes that pertain in the joint-venture. This model is illustrated by means of two case studies, one involving a case of the decay of trust into distrust, the other involving the successful building up of trustworthy relations within the joint-venture.