Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy

Understanding and Specifying Information Security Needs to Support the Delivery of High Quality Security Services

In this paper we present an approach for specifying and prioritizing information security requirements in organizations. It is important to prioritize security requirements since hundred per cent security is\ud not achievable and the limited resources available should be directed to satisfy the most important ones. We propose to explicitly link security requirements with the organization’s business vision, i.e. to provide business\ud rationale for security requirements. The rationale is then used as a basis for comparing the importance of different security requirements.\ud Furthermore we discuss how to integrate the aforementioned solution concepts into a service level management process for security services, which is an important step in IT Governance. We validate our approach by way of a focus group session

Methodology for Designing Decision Support Systems for Visualising and Mitigating Supply Chain Cyber Risk from IoT Technologies

This paper proposes a methodology for designing decision support systems for visualising and mitigating the Internet of Things cyber risks. Digital technologies present new cyber risk in the supply chain which are often not visible to companies participating in the supply chains. This study investigates how the Internet of Things cyber risks can be visualised and mitigated in the process of designing business and supply chain strategies. The emerging DSS methodology present new findings on how digital technologies affect business and supply chain systems. Through epistemological analysis, the article derives with a decision support system for visualising supply chain cyber risk from Internet of Things digital technologies. Such methods do not exist at present and this represents the first attempt to devise a decision support system that would enable practitioners to develop a step by step process for visualising, assessing and mitigating the emerging cyber risk from IoT technologies on shared infrastructure in legacy supply chain systems