Trust models in ubiquitous computing

We recapture some of the arguments for trust-based technologies in ubiquitous computing, followed by a brief survey of some of the models of trust that have been introduced in this respect. Based on this, we argue for the need of more formal and foundational trust models

MobileTrust: Secure Knowledge Integration in VANETs

Vehicular Ad hoc NETworks (VANET) are becoming popular due to the emergence of the Internet of Things and ambient intelligence applications. In such networks, secure resource sharing functionality is accomplished by incorporating trust schemes. Current solutions adopt peer-to-peer technologies that can cover the large operational area. However, these systems fail to capture some inherent properties of VANETs, such as fast and ephemeral interaction, making robust trust evaluation of crowdsourcing challenging. In this article, we propose MobileTrust—a hybrid trust-based system for secure resource sharing in VANETs. The proposal is a breakthrough in centralized trust computing that utilizes cloud and upcoming 5G technologies to provide robust trust establishment with global scalability. The ad hoc communication is energy-efficient and protects the system against threats that are not countered by the current settings. To evaluate its performance and effectiveness, MobileTrust is modelled in the SUMO simulator and tested on the traffic features of the small-size German city of Eichstatt. Similar schemes are implemented in the same platform to provide a fair comparison. Moreover, MobileTrust is deployed on a typical embedded system platform and applied on a real smart car installation for monitoring traffic and road-state parameters of an urban application. The proposed system is developed under the EU-founded THREAT-ARREST project, to provide security, privacy, and trust in an intelligent and energy-aware transportation scenario, bringing closer the vision of sustainable circular economy

On the emergent Semantic Web and overlooked issues

The emergent Semantic Web, despite being in its infancy, has already received a lotof attention from academia and industry. This resulted in an abundance of prototype systems and discussion most of which are centred around the underlying infrastructure. However, when we critically review the work done to date we realise that there is little discussion with respect to the vision of the Semantic Web. In particular, there is an observed dearth of discussion on how to deliver knowledge sharing in an environment such as the Semantic Web in effective and efficient manners. There are a lot of overlooked issues, associated with agents and trust to hidden assumptions made with respect to knowledge representation and robust reasoning in a distributed environment. These issues could potentially hinder further development if not considered at the early stages of designing Semantic Web systems. In this perspectives paper, we aim to help engineers and practitioners of the Semantic Web by raising awareness of these issues

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

Fixing feedback revision rules in online markets

Feedback withdrawal mechanisms in online markets aim to facilitate the resolution of conflicts during transactions. Yet, frequently used online feedback withdrawal rules are flawed and may backfire by inviting strategic transaction and feedback behavior. Our laboratory experiment shows how a small change in the design of feedback withdrawal rules, allowing unilateral rather than mutual withdrawal, can both reduce incentives for strategic gaming and improve coordination of expectations. This leads to less trading risk, more cooperation, and higher market efficiency.Series: Department of Strategy and Innovation Working Paper Serie

The impact of the general data protection regulation on the financial services’ industry of small European states

This paper is based on the unpublished Thesis by Magri, A. (2018). An Evaluation of the Impact of GDPR on the Local Financial Services Industry. Banking and Finance, Department of Banking and Finance, Faculty of Economics, Management and Accountancy, University of Malta, supervised by Dr. Simon GrimaPurpose: With this paper we evaluate the impact and implications of the European Union (EU) General Data Protection Regulation (GDPR) on the Financial Services Industry in small European States; specifically Malta, Slovenia, Luxembourg, Lithuania, Latvia, Estonia and Cyprus. That is, countries within the EU having less than 3 million population. Design/methodology/approach: We collected our primary data by carrying out scheduled semi-structured interviews (using WhatsApp®, Messenger® and Skype®) with 63 participants who are working directly or indirectly with GDPR in financial services between November 2018 and April 2019. The interview was structured using two impact themes, ‘Trust, Standardisation and Reputation’ and ‘Training and ‘Resources’, with 18 statements under each theme to which participants were required to answer using a 5-point Likert-scale ranging from “Strongly Disagree” to “Strongly Agree”. To answer the research questions, the empirical data collected was subjected to statistical analysis using SPSS (Version 21) namely descriptive statistics and box plots and later MANOVA, while the qualitative data was analysed using the thematic approach. Findings: We found that overall, participants feel that although GDPR has increased the work load and costs, it has helped to improve the trust, standardisation and reputation of the institutions they represent. However, this comes with some repercussions from the data subjects who are not conversant with the regulation and are apprehensive by the consents required. Originality/value: Although, all States might be represented in the decision process, the larger States usually take over and sometimes dictate the final decision. The concept of proportionality in regulations is not clean and is not effectively managed, at the disadvantage of the smaller States. Therefore, this paper is important since it voices the cries of smaller States and allows for an understanding of the impact and implications of new regulations to smaller jurisdictions, in this case within the EU.peer-reviewe

Engineering Trust-based Software Intensive Systems

Abstract of a keynote speech given at the Strategic Research Workshop on Engineering Software Intensive Systems

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information

Screening, Competition, and Job Design

In recent decades, many firms offered more discretion to their employees, often increasing the productivity of effort but also leaving more opportunities for shirking. These “high-performance work systems” are difficult to understand in terms of standard moral hazard models. We show experimentally that complementarities between high effort discretion, rent-sharing, screening opportunities, and competition are important driving forces behind these new forms of work organization. We document in particular the endogenous emergence of two fundamentally distinct types of employment strategies. Employers either implement a control strategy, which consists of low effort discretion and little or no rent-sharing, or they implement a trust strategy, which stipulates high effort discretion and substantial rent-sharing. If employers cannot screen employees, the control strategy prevails, while the possibility of screening renders the trust strategy profitable. The introduction of competition substantially fosters the trust strategy, reduces market segmentation, and leads to large welfare gains for both employers and employees