Related papers: Decentralized Trust Management: Risk Analysis and …

Non-centralized recommendation-based decision making is a central feature of several social and technological processes, such as market dynamics, peer-to-peer file-sharing and the web of trust of digital certification. We investigate the…

In this paper we study the trustworthiness of the crowd for crowdsourced software development. Through the study of literature from various domains, we present the risks that impact the trustworthiness in an enterprise context. We survey…

The abilities of traditional perimeter-based security architectures are rapidly decreasing as more enterprise assets are moved toward the cloud environment. From a security viewpoint, the Zero Trust framework can better track and block…

We propose a research initiative to explore and evaluate end-user technology, infrastructure, business imperatives, and regulatory policy to support the privacy, dignity, and market power of individual persons in the context of the emerging…

Federated sequential recommendation distributes model training across user devices so that behavioural data remains local, reducing privacy risks. Yet, this setting introduces two intertwined difficulties. On the one hand, individual…

One rapidly growing application of Internet of Things (IoT) is the protection of public health and well-being through enabling environmental monitoring services. In particular, an IoT-enabled health/accessibility monitoring service (HAMS)…

The paper provides a framework for the assessment and optimization of the total risk of complex distributed systems. The framework takes into account the risk of each agent, which may arise from heterogeneous sources, as well as the risk…

In order to collaborate efficiently with unknown partners in cooperative control settings, adaptation of the partners based on online experience is required. The rather general and widely applicable control setting, where each cooperation…

Imagine a group of citizens willing to collectively contribute their personal data for the common good to produce socially useful information, resulting from data analytics or machine learning computations. Sharing raw personal data with a…

Trust is often conveyed through delegation, or through recommendation. This makes the trust authorities, who process and publish trust recommendations, into an attractive target for attacks and spoofing. In some recent empiric studies, this…

This thesis addresses the problem of providing trusted individuals with confidential information about other individuals, in particular, granting access to databases of personal records using the World-Wide Web. It proposes an access rights…

Many intelligent transportation systems are multi-agent systems, i.e., both the traffic participants and the subsystems within the transportation infrastructure can be modeled as interacting agents. The use of AI-based methods to achieve…

Security features must be addressed when escalating a distributed database. The choice between the object oriented and the relational data model, several factors should be considered. The most important of these factors are single and…

Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality…

Indirect reciprocity is a foundational mechanism of human cooperation. Existing models of indirect reciprocity fail to robustly support social cooperation: image scoring models fail to provide robust incentives, while social standing models…

Cooperative decentralized learning relies on direct information exchange between communicating agents, each with access to locally available datasets. The goal is to agree on model parameters that are optimal over all data. However, sharing…

Data privacy and sharing has always been a critical issue when trying to build complex deep learning-based systems to model data. Facilitation of a decentralized approach that could take benefit from data across multiple nodes while not…

Current contingency reserve criteria ignore the likelihood of individual contingencies and, thus, their impact on system reliability and risk. This paper develops an iterative approach, inspired by the current security-constrained unit…

Trustworthy Artificial Intelligence solutions are essential in today's data-driven applications, prioritizing principles such as robustness, safety, transparency, explainability, and privacy among others. This has led to the emergence of…

In decentralized cooperative multi-agent reinforcement learning, agents can aggregate information from one another to learn policies that maximize a team-average objective function. Despite the willingness to cooperate with others, the…