Location-based recommendation systems are becoming increasingly ubiquitous in the march toward fully tailored user experiences. Applications like Google Maps, Strava, Uber, Hinge, and many others utilize location data as key material for providing contextual recommendations. Rising concerns about usage and misuse of location data have arisen in recent years. We situate this paper within design’s future-oriented nature, critically speculating possible futures with location-based recommenders. We propose a refinement of current approaches and speculative design for engaging domain experts in co-speculation through the use of tailored, high-fidelity, critical design fiction films. In this case study, we lay preliminary insights, including a widespread sense of fatalism, self-described lack of agency, and underlying individualist ideologies driving development and deployment of these systems. We also reflect on the process of creating four critical design fiction videos, their use in 11 guided co-speculation sessions, and implications for their use in gathering rich qualitative data, creating space for reflection, prompting stories and personal connection, and unpacking experts' views on complex wicked problems.
Fertility trackers are popular for self-monitoring menstrual cycles and managing other aspects of reproductive or sexual health. However, the intimate nature of fertility tracking raises particular concerns about potential data (mis)use. Our study deepens understandings of fertility tracker data sharing and presents co-created mechanisms to enhance user agency over their data in intimate contexts. To achieve this, we first analysed the network transmissions from eight fertility tracker products, observing that many data transmissions appear to be tied to particular uses of the tracker and that the products communicate with endpoints associated with various organisations across different countries. This raises concerns about how intimate data is governed, used, and shared. To understand user attitudes towards data sharing in intimate contexts, we then conducted a survey exploring factors influencing user data sharing preferences. Our findings reveal that users desire transparency and control mechanisms and that their willingness to share data is influenced by contextual factors, including the third parties involved, the purposes of data collection, and the sensitivity of the data. Building on these findings, we worked with users to co-design ten concrete mechanisms for enhancing data transparency and control throughout fertility tracker product usage lifecycles. In all, our mixed-method study provides an in-depth understanding of fertility tracker data flows and preferences and proposes actionable mechanisms designers can utilise to support and protect data rights in intimate data ecosystems.
Personally identifiable information (PII) is a fundamental concept in privacy research and regulations. Understanding users' perspectives on PII is critical, as their understanding of PII can significantly affect their privacy decisions and practices. While much research has explored users’ privacy perceptions and disclosure preferences regarding PII, less attention has been focused on how users internally define and conceptualize PII. In this study, we conducted interviews with 32 participants to investigate their conceptualization and understanding of PII, using period and fertility tracking apps as the context. Our findings reveal how users perceive the processes and contexts through which personal information, by becoming identifiable, transitions into PII, as well as concerns about data sharing and misuse in these apps. We conclude by advocating for addressing the misalignment between users' perceptions of PII and the regulatory protections and privacy designs surrounding it.
GPTs are customized LLM apps built on OpenAI's large language model. Any individual or organization can use and create GPTs without needing programming skills. However, the rapid proliferation of over three million GPTs has raised significant privacy concerns. To explore the privacy perspectives of users and creators, we interviewed 23 GPT users with varying levels of creation experience. Our findings reveal blurred lines between user and creator roles and their understanding of GPT data flows. Participants raised concerns about data handling during collection, processing, and dissemination, alongside the lack of privacy regulations. Creators also worried about loss of their proprietary knowledge. In response, participants adopted practices like self-censoring input, evaluating GPT actions, and minimizing usage traces. Focusing on the dual role of user-creators, we find that expertise and responsibility shape privacy perceptions. Based on these insights, we propose practical recommendations to improve data transparency and platform regulations.
Adherence to data protection measures such as pseudonymization or anonymization is critical in human subjects research because it has a direct impact on the confidentiality of participants' sensitive information, trust in research practices, and compliance with ethical and legal standards. Regulations such as the General Data Protection Regulation (GDPR) and guarantees made by researchers in informed consent forms mandate strict protocols for data security. However, compliance with these is not always straightforward. To gain qualitative insights into data protection practices in the field of Usable Security and Privacy (USP), we conducted interviews with 22 practitioners (five professors, eight researchers, nine data protection officers) and one focus group with five researchers. Overall, our results show a high awareness of ethical and legal responsibilities but highlight many practical and procedural issues. Based on these, we make concrete recommendations on how to improve the protection of personal data in research.
Third parties track users' web browsing activities, raising privacy concerns. Tracking protection extensions prevent this, but their influence on privacy protection beliefs shaped by narratives remains uncertain. This paper investigates users' misperception of tracking protection offered by browser plugins. Our study explores how different narratives influence users' perceived privacy protection by examining three tracking protection extension narratives: no protection, functional protection, and a placebo. In a study (N=36), participants evaluated their anticipated protection during a hotel booking process, influenced by the narrative about the plugin's functionality. However, participants viewed the same website without tracking protection adaptations. We show that users feel more protected when informed they use a functional or placebo extension, compared to no protection. Our findings highlight the deceptive nature of misleading privacy tools, emphasizing the need for greater transparency to prevent users from a false sense of protection, as such misleading tools negatively affect user study results.
Location privacy leaks can lead to unauthorised tracking, identity theft, and targeted attacks, compromising personal security and privacy. This study explores LLM-powered location privacy leaks associated with photo sharing on social media, focusing on user awareness, attitudes, and opinions. We developed and introduced an LLM-powered location privacy intervention app to 19 participants, who used it over a two-week period. The app prompted users to reflect on potential privacy leaks that a widely available LLM could easily detect, such as visual landmarks & cues that could reveal their location, and provided ways to conceal this information. Through in-depth interviews, we found that our intervention effectively increased users’ awareness of location privacy and the risks posed by LLMs. It also encouraged users to consider the importance of maintaining control over their privacy data and sparked discussions about the future of location privacy-preserving technologies. Based on these insights, we offer design implications to support the development of future user-centred, location privacy-preserving technologies for social media photos.