IoT trust and reputation: a survey and taxonomy

IoT is one of the fastest-growing technologies and it is estimated that more than a billion devices would be utilized across the globe by the end of 2030. To maximize the capability of these connected entities, trust and reputation among IoT entities is essential. Several trust management models have been proposed in the IoT environment; however, these schemes have not fully addressed the IoT devices features, such as devices role, device type and its dynamic behavior in a smart environment. As a result, traditional trust and reputation models are insufficient to tackle these characteristics and uncertainty risks while connecting nodes to the network. Whilst continuous study has been carried out and various articles suggest promising solutions in constrained environments, research on trust and reputation is still at its infancy. In this paper, we carry out a comprehensive literature review on state-of-the-art research on the trust and reputation of IoT devices and systems. Specifically, we first propose a new structure, namely a new taxonomy, to organize the trust and reputation models based on the ways trust is managed. The proposed taxonomy comprises of traditional trust management-based systems and artificial intelligence-based systems, and combine both the classes which encourage the existing schemes to adapt these emerging concepts. This collaboration between the conventional mathematical and the advanced ML models result in design schemes that are more robust and efficient. Then we drill down to compare and analyse the methods and applications of these systems based on community-accepted performance metrics, e.g. scalability, delay, cooperativeness and efficiency. Finally, built upon the findings of the analysis, we identify and discuss open research issues and challenges, and further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin

COBRA: Context-aware Bernoulli Neural Networks for Reputation Assessment

Trust and reputation management (TRM) plays an increasingly important role in large-scale online environments such as multi-agent systems (MAS) and the Internet of Things (IoT). One main objective of TRM is to achieve accurate trust assessment of entities such as agents or IoT service providers. However, this encounters an accuracy-privacy dilemma as we identify in this paper, and we propose a framework called Context-aware Bernoulli Neural Network based Reputation Assessment (COBRA) to address this challenge. COBRA encapsulates agent interactions or transactions, which are prone to privacy leak, in machine learning models, and aggregates multiple such models using a Bernoulli neural network to predict a trust score for an agent. COBRA preserves agent privacy and retains interaction contexts via the machine learning models, and achieves more accurate trust prediction than a fully-connected neural network alternative. COBRA is also robust to security attacks by agents who inject fake machine learning models; notably, it is resistant to the 51-percent attack. The performance of COBRA is validated by our experiments using a real dataset, and by our simulations, where we also show that COBRA outperforms other state-of-the-art TRM systems.Comment: To be published in the Proceedings of AAAI, Feb 202

Notes for a Political Epistemology of Algorithms

Algorithms are everywhere they watch us, they give us advice, sometimes they take autonomous decisions. They are agents mediating between reality and us by virtue of their capability to treat huge amounts of data and to see patterns that are invisible to us. It is pretty clear that they are political actors by virtue of their epistemic features. In this article, I try to put together these two points and outline a political epistemology of algorithms. Firstly, I define political epistemology as concerned with epistemic performances that are essentially situated and therefore political all the way down. Secondly, I show that, in this sense, an epistemic analysis of algorithms cannot help being a piece of political epistemology

Three Essays on Organizational Socialization from Dissimilar Employee’s Perspective

This dissertation consists of 3 essays all of which seek to examine the socialization experiences of newcomers who perceive themselves to be dissimilar from their work colleagues before, during, and after they start their jobs. I define the perceived dissimilarity as the degree to which individuals perceived themselves to be different from most others in the organization. The first essay provides a comprehensive review of the theoretical and empirical literature on organizational socialization, identifies four dominant theoretical perspectives and their gaps, and sets the stage for the research model developed for this dissertation. At the end of the first essay, the integrative model of organizational socialization is introduced, which incorporates important elements of the four influential research perspectives to examine the socialization processes and outcomes of newcomers who perceive themselves to be dissimilar to their work colleagues during the anticipatory stage (pre-organizational entry), accommodation stage (immediately following organizational entry), and role management stages (six months after starting new work role). The second essay focuses on understanding the anticipatory (pre-organizational entry) stage of dissimilar newcomers’ socialization experiences. Specifically, it examines the interaction between individual and contextual factors on proactive socialization behaviors of newcomers’ who perceive themselves to be dissimilar from their work colleagues. The third essay focuses on understanding the socialization experiences of newcomers’ who perceive themselves to be dissimilar from their work colleagues during the last two stages of the organizational socialization process (accommodation and role management stage). Specifically, it examines the interaction between individual and contextual factors on newcomers\u27 proactive socialization behaviors and adjustment and attitudinal outcomes one month and six months after starting their new work role. Data is collected at 4 times (pre-entry, 2 weeks after entry, 3 months after entry, 6 months after entry) by collaborating with Qualtrics data collection team. The final sample size consists of 80 people who had an offer but had not started working at time 1. The theoretical and practical implications of my research are discussed at the end of the essays