arXiv subjects

Simone Fischer-Hübner

Publications and source records attributed to Simone Fischer-Hübner.

8 recordsLinked to original sources

arXiv2025

Towards Usable Privacy Management for IoT TAPs: Deriving Privacy Clusters and Preference Profiles

IoT Trigger-Action Platforms (TAPs) typically offer coarse-grained permission controls. Even when fine-grained controls are available, users are likely overwhelmed by the complexity of setting privacy preferences. This paper contributes to usable privacy management for TAPs by deriving privacy clusters and profiles for different types of users that can be semi-automatically assigned or suggested to them. We developed and validated a questionnaire, based on users' privacy concerns regarding confidentiality and control and their requirements towards transparency in TAPs. In an online study (N=301), where participants were informed about potential privacy risks, we clustered users by their privacy concerns and requirements into Basic, Medium and High Privacy clusters. These clusters were then characterized by the users' data sharing preferences, based on a factorial vignette approach, considering the data categories, the data recipient types, and the purpose of data sharing. Our findings show three distinct privacy profiles, providing a foundation for more usable privacy controls in TAPs.

cs.HC↗

arXiv2025

Cybersecurity Competence for Organisations in Inner Scandinavia

A rapidly growing number of cybersecurity threats and incidents demands that Swedish organisations increase their efforts to improve their cybersecurity capacities. This paper presents results from interviews and a prior survey with key representatives from enterprises and public sector organisations in the Swedish region of Värmland in Inner Scandinavia, examining their cybersecurity readiness and needs for education and competence development. We discuss the generalizability of our findings and the extent to which they may be specific to Sweden and Värmland, and we conclude by proposing efforts to strengthen cybersecurity competences in Inner Scandinavia.

cs.CR↗

arXiv2025

AI-driven Personalized Privacy Assistants: a Systematic Literature Review

In recent years, several personalized assistants based on AI have been researched and developed to help users make privacy-related decisions. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can provide significant benefits for users, who might otherwise struggle with making decisions about their personal data in online environments that often overload them with different privacy decision requests. So far, no studies have systematically investigated the emerging topic of AI-driven PPAs, classifying their underlying technologies, architecture and features, including decision types or the accuracy of their decisions. To fill this gap, we present a Systematic Literature Review (SLR) to map the existing solutions found in the scientific literature, which allows reasoning about existing approaches and open challenges for this research field. We screened several hundred unique research papers over the recent years (2013-2025), constructing a classification from 41 included papers. As a result, this SLR reviews several aspects of existing research on AI-driven PPAs in terms of types of publications, contributions, methodological quality, and other quantitative insights. Furthermore, we provide a comprehensive classification for AI-driven PPAs, delving into their architectural choices, system contexts, types of AI used, data sources, types of decisions, and control over decisions, among other facets. Based on our SLR, we further underline the research gaps and challenges and formulate recommendations for the design and development of AI-driven PPAs as well as avenues for future research.

cs.CY↗

arXiv2024

Privacy Impact Assessments in the Wild: A Scoping Review

Privacy Impact Assessments (PIAs) offer a systematic process for assessing the privacy impacts of a project or system. As a privacy engineering strategy, PIAs are heralded as one of the main approaches to privacy by design, supporting the early identification of threats and controls. However, there is still a shortage of empirical evidence on their uptake and proven effectiveness in practice. To better understand the current state of literature and research, this paper provides a comprehensive Scoping Review (ScR) on the topic of PIAs "in the wild", following the well-established Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. As a result, this ScR includes 45 studies, providing an extensive synthesis of the existing body of knowledge, classifying types of research and publications, appraising the methodological quality of primary research, and summarising the positive and negative aspects of PIAs in practice, as reported by studies. This ScR also identifies significant research gaps (e.g., evidence gaps from contradictory results and methodological gaps from research design deficiencies), future research pathways, and implications for researchers, practitioners, and policymakers developing and evaluating PIA frameworks. As we conclude, there is still a significant need for more primary research on the topic, both qualitative and quantitative. A critical appraisal of qualitative studies (n=28) revealed deficiencies in the methodological quality, and only four quantitative studies were identified, suggesting that current primary research remains incipient. Nonetheless, PIAs can be regarded as a prominent sub-area in the broader field of Empirical Privacy Engineering, warranting further research toward more evidence-based practices.

cs.CR↗

arXiv2023

Tapping into Privacy: A Study of User Preferences and Concerns on Trigger-Action Platforms

The Internet of Things (IoT) devices are rapidly increasing in popularity, with more individuals using Internet-connected devices that continuously monitor their activities. This work explores privacy concerns and expectations of end-users related to Trigger-Action platforms (TAPs) in the context of the Internet of Things (IoT). TAPs allow users to customize their smart environments by creating rules that trigger actions based on specific events or conditions. As personal data flows between different entities, there is a potential for privacy concerns. In this study, we aimed to identify the privacy factors that impact users' concerns and preferences for using IoT TAPs. To address this research objective, we conducted three focus groups with 15 participants and we extracted nine themes related to privacy factors using thematic analysis. Our participants particularly prefer to have control and transparency over the automation and are concerned about unexpected data inferences, risks and unforeseen consequences for themselves and for bystanders that are caused by the automation. The identified privacy factors can help researchers derive predefined and selectable profiles of privacy permission settings for IoT TAPs that represent the privacy preferences of different types of users as a basis for designing usable privacy controls for IoT TAPs.

cs.CY↗

arXiv2023

Automating privacy decisions -- where to draw the line?

Users are often overwhelmed by privacy decisions to manage their personal data, which can happen on the web, in mobile, and in IoT environments. These decisions can take various forms -- such as decisions for setting privacy permissions or privacy preferences, decisions responding to consent requests, or to intervene and ``reject'' processing of one's personal data --, and each can have different legal impacts. In all cases and for all types of decisions, scholars and industry have been proposing tools to better automate the process of privacy decisions at different levels, in order to enhance usability. We provide in this paper an overview of the main challenges raised by the automation of privacy decisions, together with a classification scheme of the existing and envisioned work and proposals addressing automation of privacy decisions.

cs.CY↗

arXiv2021

A Multidisciplinary Definition of Privacy Labels: The Story of Princess Privacy and the Seven Helpers

Privacy is currently in distress and in need of rescue, much like princesses in the all-familiar fairytales. We employ storytelling and metaphors from fairytales to make reader-friendly and streamline our arguments about how a complex concept of Privacy Labeling (the 'knight in shining armor') can be a solution to the current state of Privacy (the 'princess in distress'). We give a precise definition of Privacy Labeling (PL), painting a panoptic portrait from seven different perspectives (the 'seven helpers'): Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological, and Multidisciplinary. We describe a common vision, proposing several important 'traits of character' of PL as well as identifying 'undeveloped potentialities', i.e., open problems on which the community can focus. More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how PL should be and look like in order to address these needs. Throughout the paper, we highlight goals, characteristics, open problems, and starting points for creating, what we consider to be, the ideal PL. In the end we present three approaches to establish and manage PL, through: self-evaluations, certifications, or community endeavors. Based on these, we sketch a roadmap for future developments.

cs.CR↗

arXiv2020

Making GDPR Usable: A Model to Support Usability Evaluations of Privacy

We introduce a new model for evaluating privacy that builds on the criteria proposed by the EuroPriSe certification scheme by adding usability criteria. Our model is visually represented through a cube, called Usable Privacy Cube (or UP Cube), where each of its three axes of variability captures, respectively: rights of the data subjects, privacy principles, and usable privacy criteria. We slightly reorganize the criteria of EuroPriSe to fit with the UP Cube model, i.e., we show how EuroPriSe can be viewed as a combination of only rights and principles, forming the two axes at the basis of our UP Cube. In this way we also want to bring out two perspectives on privacy: that of the data subjects and, respectively, that of the controllers/processors. We define usable privacy criteria based on usability goals that we have extracted from the whole text of the General Data Protection Regulation. The criteria are designed to produce measurements of the level of usability with which the goals are reached. Precisely, we measure effectiveness, efficiency, and satisfaction, considering both the objective and the perceived usability outcomes, producing measures of accuracy and completeness, of resource utilization (e.g., time, effort, financial), and measures resulting from satisfaction scales. In the long run, the UP Cube is meant to be the model behind a new certification methodology capable of evaluating the usability of privacy, to the benefit of common users. For industries, considering also the usability of privacy would allow for greater business differentiation, beyond GDPR compliance.

cs.HC↗