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Farzaneh Karegar

Publications and source records attributed to Farzaneh Karegar.

4 recordsLinked to original sources

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↗

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↗

"I will never pay for this" Perception of fairness and factors affecting behaviour on 'pay-or-ok' models

The rise of cookie paywalls ('pay-or-ok' models) has prompted growing debates around the right to privacy and data protection, monetisation, and the legitimacy of user consent. Despite their increasing use across sectors, limited research has explored how users perceive these models or what shapes their decisions to either consent to tracking or pay. To address this gap, we conducted four focus groups (n= 14) to examine users' perceptions of cookie paywalls, their judgments of fairness, and the conditions under which they might consider paying, alongside a legal analysis within the EU data protection legal framework. Participants primarily viewed cookie paywalls as profit-driven, with fairness perceptions varying depending on factors such as the presence of a third option beyond consent or payment, transparency of data practices, and the authenticity or exclusivity of the paid content. Participants voiced expectations for greater transparency, meaningful control over data collection, and less coercive alternatives, such as contextual advertising or "reject all" buttons. Although some conditions, including trusted providers, exclusive content, and reasonable pricing, could make participants consider paying, most expressed reluctance or unwillingness to do so. Crucially, our findings raise concerns about economic exclusion, where privacy and data protection might end up becoming a privilege rather than fundamental rights. Consent given under financial pressure may not meet the standard of being freely given, as required by the GDPR. To address these concerns, we recommend user-centred approaches that enhance transparency, reduce coercion, ensure the value of paid content, and explore inclusive alternatives. These measures are essential for supporting fairness, meaningful choice, and user autonomy in consent-driven digital environments.

cs.CY↗

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↗