Post-Roe, people capable of pregnancy face fragmented reproductive privacy landscapes in the United States (U.S.), with risks spanning legal, digital, and interpersonal domains. These conditions demand new forms of privacy guidance. We analyzed 212 reproductive health zines— a DIY, subversive, and collectively produced media genre—to understand how they communicate reproductive health information. Zines foreground embodied, first-person narratives interwoven with historical context, medical guidance, and activist messaging. We argue their use of subversive or alternative medical knowledge enhanced credibility in contexts of low institutional trust. While some zines offer digital privacy strategies, many focus on avoiding institutional exposure altogether. These emotionally resonant, context-sensitive accounts illustrate threat models attuned to entangled risks of interpersonal betrayal, legal precarity, and surveillance. We conclude with design implications for how zines might better support people navigating reproductive risk through what we call narrative threat modeling—a situated practice that communicates privacy strategies through story, tone, and form rather than technical instructions or prescriptive checklists.

Online targeted ads are those shown only to certain users based on interests, demographics, or behaviors. Because targeted ads raise many privacy concerns, many platforms provide ad transparency systems (ATSs) to inform users about this practice. To better understand what current ATSs are communicating to users—and how—we first taxonomized the design and content of 22 of the most popular English-language websites' ATSs as presented to users in the United States. We found substantial differences across ATSs in both the prevalence of transparency-enhancing features (e.g., whether they show users what has been inferred about them) and the presentation of information (e.g., the terminology used, where settings are located). Across all platforms, however, we observed consistent ambiguity about what data is used to target ads and the actual impact of altering settings. To gauge how these different design choices impact users, we conducted an online user study in which 198 participants used their own account to explore the ATS of one of eight representative platforms. We found that many of the questions participants hoped the ATS would answer remained unanswered after exploring the ATS. More broadly, participants found current ATSs simultaneously complex and lacking key details. We pinpoint ATS design decisions that best support users.

The landscape of digital privacy risks faced by individuals seeking abortions has grown increasingly complex following the overturn of Roe v. Wade. Reproductive healthcare providers are uniquely positioned to offer critical privacy guidance. We conducted interviews with 22 reproductive healthcare providers across the U.S. to explore their perceptions of privacy threats for abortion-seeking patients and the types of guidance they provide. Our findings show that providers are most concerned about privacy risks for vulnerable patients—minors, individuals seeking gender-affirming care, and those in abusive relationships—particularly regarding information that could be intercepted by people close to them, such as partners or relatives. However, providers generally do not perceive government surveillance or hostile actors as major threats to abortion-seeking patients. We conclude with an updated notion of informed consent and preliminary recommendations for ways healthcare providers can revise their threat models to better support the privacy of abortion-seeking patients.

Exploration of Internet of Things (IoT) security often focuses on threats posed by external and technically-skilled attackers. While it is important to understand these most extreme cases, it is equally important to understand the most likely risks of harm posed by smart device ownership. In this paper, we explore how smart devices are misused — used without permission in a manner that causes harm — by device owners’ everyday associates such as friends, family, and romantic partners. In a preliminary characterization survey (n = 100), we broadly capture the kinds of unauthorized use and misuse incidents participants have experienced or engaged in. Then, in a prevalence survey (n = 483), we assess the prevalence of these incidents in a demographically-representative population. Our findings show that unauthorized use of smart devices is widespread (experienced by 43% of participants), and that misuse is also common (experienced by at least 19% of participants). However, highly individual factors determine whether these unauthorized use events constitute misuse. Through a focus on everyday abuses, this work sheds light on the most prevalent security and privacy threats faced by smart-home owners today.

Although we have seen a proliferation of algorithms for recommending visualizations, these algorithms are rarely compared with one another, making it difficult to ascertain which algorithm is best for a given visual analysis scenario. Though several formal frameworks have been proposed in response, we believe this issue persists because visualization recommendation algorithms are inadequately specified from an evaluation perspective. In this paper, we propose an evaluation-focused framework to contextualize and compare a broad range of visualization recommendation algorithms. We present the structure of our framework, where algorithms are specified using three components: (1) a graph representing the full space of possible visualization designs, (2) the method used to traverse the graph for potential candidates for recommendation, and (3) an oracle used to rank candidate designs. To demonstrate how our framework guides the formal comparison of algorithmic performance, we not only theoretically compare five existing representative recommendation algorithms, but also empirically compare four new algorithms generated based on our findings from the theoretical comparison. Our results show that these algorithms behave similarly in terms of user performance, highlighting the need for more rigorous formal comparisons of recommendation algorithms to further clarify their benefits in various analysis scenarios.