Biblio
Internet of Things (IoT) is an evolving research area for the last two decades. The integration of the IoT and social networking concept results in developing an interdisciplinary research area called the Social Internet of Things (SIoT). The SIoT is dominant over the traditional IoT because of its structure, implementation, and operational manageability. In the SIoT, devices interact with each other independently to establish a social relationship for collective goals. To establish trustworthy relationships among the devices significantly improves the interaction in the SIoT and mitigates the phenomenon of risk. The problem is to choose a trustworthy node who is most suitable according to the choice parameters of the node. The best-selected node by one node is not necessarily the most suitable node for other nodes, as the trustworthiness of the node is independent for everyone. We employ some theoretical characterization of the soft-set theory to deal with this kind of decision-making problem. In this paper, we developed a weighted based trustworthiness ranking model by using soft set theory to evaluate the trustworthiness in the SIoT. The purpose of the proposed research is to reduce the risk of fraudulent transactions by identifying the most trusted nodes.
Distributed consensus is a prototypical distributed optimization and decision making problem in social, economic and engineering networked systems. In collaborative applications investigating the effects of adversaries is a critical problem. In this paper we investigate distributed consensus problems in the presence of adversaries. We combine key ideas from distributed consensus in computer science on one hand and in control systems on the other. The main idea is to detect Byzantine adversaries in a network of collaborating agents who have as goal reaching consensus, and exclude them from the consensus process and dynamics. We describe a novel trust-aware consensus algorithm that integrates the trust evaluation mechanism into the distributed consensus algorithm and propose various local decision rules based on local evidence. To further enhance the robustness of trust evaluation itself, we also introduce a trust propagation scheme in order to take into account evidences of other nodes in the network. The resulting algorithm is flexible and extensible, and can incorporate more complex designs of decision rules and trust models. To demonstrate the power of our trust-aware algorithm, we provide new theoretical security performance results in terms of miss detection and false alarm rates for regular and general trust graphs. We demonstrate through simulations that the new trust-aware consensus algorithm can effectively detect Byzantine adversaries and can exclude them from consensus iterations even in sparse networks with connectivity less than 2f+1, where f is the number of adversaries.
This work takes a novel approach to classifying the behavior of devices by exploiting the single-purpose nature of IoT devices and analyzing the complexity and variance of their network traffic. We develop a formalized measurement of complexity for IoT devices, and use this measurement to precisely tune an anomaly detection algorithm for each device. We postulate that IoT devices with low complexity lead to a high confidence in their behavioral model and have a correspondingly more precise decision boundary on their predicted behavior. Conversely, complex general purpose devices have lower confidence and a more generalized decision boundary. We show that there is a positive correlation to our complexity measure and the number of outliers found by an anomaly detection algorithm. By tuning this decision boundary based on device complexity we are able to build a behavioral framework for each device that reduces false positive outliers. Finally, we propose an architecture that can use this tuned behavioral model to rank each flow on the network and calculate a trust score ranking of all traffic to and from a device which allows the network to autonomously make access control decisions on a per-flow basis.
Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.
Recently, the increase of different services makes the design of routing protocols more difficult in mobile ad hoc networks (MANETs), e.g., how to guarantee the QoS of different types of traffics flows in MANETs with resource constrained and malicious nodes. opportunistic routing (OR) can make full use of the broadcast characteristics of wireless channels to improve the performance of MANETs. In this paper, we propose a traffic-differentiated secure opportunistic routing from a game theoretic perspective, DSOR. In the proposed scheme, we use a novel method to calculate trust value, considering node's forwarding capability and the status of different types of flows. According to the resource status of the network, we propose a service price and resource price for the auction model, which is used to select optimal candidate forwarding sets. At the same time, the optimal bid price has been proved and a novel flow priority decision for transmission is presented, which is based on waiting time and requested time. The simulation results show that the network lifetime, packet delivery rate and delay of the DSOR are better than existing works.
The collaborative nature of content development has given rise to the novel problem of multiple ownership in access control, such that a shared resource is administrated simultaneously by co-owners who may have conflicting privacy preferences and/or sharing needs. Prior work has focused on the design of unsupervised conflict resolution mechanisms. Driven by the need for human consent in organizational settings, this paper explores interactive policy negotiation, an approach complementary to that of prior work. Specifically, we propose an extension of Relationship-Based Access Control (ReBAC) to support multiple ownership, in which a policy negotiation protocol is in place for co-owners to come up with and give consent to an access control policy in a structured manner. During negotiation, the draft policy is assessed by formally defined availability criteria: to the second level of the polynomial hierarchy. We devised two algorithms for verifying policy satisfiability, both employing a modern SAT solver for solving subproblems. The performance is found to be adequate for mid-sized organizations.
Different wireless Peer-to-Peer (P2P) routing protocols rely on cooperative protocols of interaction among peers, yet, most of the surveyed provide little detail on how the peers can take into consideration the peers' reliability for improving routing efficiency in collaborative networks. Previous research has shown that in most of the trust and reputation evaluation schemes, the peers' rating behaviour can be improved to include the peers' attributes for understanding peers' reliability. This paper proposes a reliability based trust model for dynamic trust evaluation between the peers in P2P networks for collaborative routing. Since the peers' routing attributes vary dynamically, our proposed model must also accommodate the dynamic changes of peers' attributes and behaviour. We introduce peers' buffers as a scaling factor for peers' trust evaluation in the trust and reputation routing protocols. The comparison between reliability and non-reliability based trust models using simulation shows the improved performance of our proposed model in terms of delivery ratio and average message latency.
Globally distributed collaboration requires cooperation and trust among team members. Current research suggests that informal, non-work related communication plays a positive role in developing cooperation and trust. However, the way in which teams connect, i.e. via a social network, greatly influences cooperation and trust development. The study described in this paper employs agent-based modeling and simulation to investigate the cooperation and trust development with the presence of informal, non-work-related communication in networked teams. Leveraging game theory, we present a model of how an individual makes strategic decisions when interacting with her social network neighbors. The results of simulation on a pseudo scale-free network reveal the conditions under which informal communication has an impact, how different network degree distributions affect efficient trust and cooperation development, and how it is possible to "seed" trust and cooperation development amongst individuals in specific network positions. This study is the first to use agent-based modeling and simulation to examine the relationships between scale-free networks' topological features (degree distribution), cooperation and trust development, and informal communication.
Trust plays an important role in various user-facing systems and applications. It is particularly important in the context of decision support systems, where the system's output serves as one of the inputs for the users' decision making processes. In this work, we study the dynamics of explicit and implicit user trust in a simulated automated quality monitoring system, as a function of the system accuracy. We establish that users correctly perceive the accuracy of the system and adjust their trust accordingly.
In light of the prevalent trend towards dense HetNets, the conventional coupled user association, where mobile device uses the same base station (BS) for both uplink and downlink traffic, is being questioned and the alternative and more general downlink/uplink decoupling paradigm is emerging. We focus on designing an effective user association mechanism for HetNets with downlink/uplink decoupling, which has started to receive more attention. We use a combination of matching theory and stochastic geometry. We model the problem as a matching with contracts game by drawing an analogy with the hospital-doctor matching problem. In our model, we use stochastic geometry to derive a closed-form expression for matching utility function. Our model captures different objectives between users in the uplink/downlink directions and also from the perspective of BSs. Based on this game model, we present a matching algorithm for decoupled uplink/downlink user association that results in a stable allocation. Simulation results demonstrate that our approach provides close-to-optimal performance, and significant gains over alternative approaches for user association in the decoupled context as well as the traditional coupled user association; these gains are a result of the holistic nature of our approach that accounts for the additional cost associated with decoupling and inter-dependence between uplink and downlink associations. Our work is also the first in the wireless communications domain to employ matching with contracts approach.
Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationship is model as 2-D vector containing both trust and certainty information. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.