arXiv subjects
Publications and source records attributed to Ruba Abu-Salma.
We present key challenges and future research directions in the security and privacy of agentic AI, based on a horizon-scanning exercise that brought together thirty leading international experts from academia, industry, and government to engage in focused discussions and collaborative exercises on the emerging risks associated with the growing agency of AI.
The rapid adoption of generative AI (GenAI) chatbots has reshaped access to sexual and reproductive health (SRH) information, particularly following the overturning of Roe v. Wade, as individuals assigned female at birth increasingly turn to online sources. However, existing research remains largely model-centered, paying limited attention to user privacy and safety. We conducted semi-structured interviews with 18 U.S.-based participants from both restrictive and non-restrictive states who had used GenAI chatbots to seek SRH information. Adoption was influenced by perceived utility, usability, credibility, accessibility, and anthropomorphism, and many participants disclosed sensitive personal SRH details. Participants identified multiple privacy risks, including excessive data collection, government surveillance, profiling, model training, and data commodification. While most participants accepted these risks in exchange for perceived utility, abortion-related queries elicited heightened safety concerns. Few participants employed protective strategies beyond minimizing disclosures or deleting data. Based on these findings, we offer design and policy recommendations, such as health-specific features and stronger moderation practices, to enhance privacy and safety in GenAI-supported SRH information seeking.
The growing adoption of AI-driven smart home devices has introduced new privacy risks for domestic workers (DWs), who are frequently monitored in employers' homes while also using smart devices in their own households. We conducted semi-structured interviews with 18 UK-based DWs and performed a human-centered threat modeling analysis of their experiences through the lens of Communication Privacy Management (CPM). Our findings extend existing threat models beyond abstract adversaries and single-household contexts by showing how AI analytics, residual data logs, and cross-household data flows shaped the privacy risks faced by participants. In employer-controlled homes, AI-enabled features and opaque, agency-mediated employment arrangements intensified surveillance and constrained participants' ability to negotiate privacy boundaries. In their own homes, participants had greater control as device owners but still faced challenges, including gendered administrative roles, opaque AI functionalities, and uncertainty around data retention. We synthesize these insights into a sociotechnical threat model that identifies DW agencies as institutional adversaries and maps AI-driven privacy risks across interconnected households, and we outline social and practical implications for strengthening DW privacy and agency.
Bystander privacy in smart homes has been widely studied in Western contexts, yet it remains underexplored in non-Western countries such as China. In this study, we analyze 49 Chinese smart home apps using a mixed-methods approach, including privacy policy review, UX/UI evaluation, and assessment of Apple App Store privacy labels. While most apps nominally comply with national regulations, we identify significant gaps between written policies and actual implementation. Our traceability analysis highlights inconsistencies in data controls and a lack of transparency in data-sharing practices. Crucially, bystander privacy -- particularly for visitors and non-user individuals -- is largely absent from both policy documents and interface design. Additionally, discrepancies between privacy labels and actual data practices threaten user trust and undermine informed consent. We provide design recommendations to strengthen bystander protections, improve privacy-oriented UI transparency, and enhance the credibility of privacy labels, supporting the development of inclusive smart home ecosystems in non-Western contexts.
As more young women in China live alone, they navigate entangled privacy, security, and safety (PSS) risks across smart homes, online platforms, and public infrastructures. Drawing on six participatory threat modeling (PTM) workshops (n = 33), we present a human-centered threat model that illustrates how digitally facilitated physical violence, digital harassment and scams, and pervasive surveillance by individuals, companies, and the state are interconnected and mutually reinforcing. We also document four mitigation strategies employed by participants: smart home device configurations, boundary management, sociocultural practices, and social media tactics--each of which can introduce new vulnerabilities and emotional burdens. Based on these insights, we developed a digital PSS guidebook for young women living alone (YWLA) in China. We further propose actionable design implications for smart home devices and social media platforms, along with policy and legal recommendations and directions for educational interventions.
Previous research has explored the privacy needs and concerns of device owners, primary users, and different bystander groups with regard to smart home devices like security cameras, smart speakers, and hubs, but little is known about the privacy views and practices of smart home product teams, particularly those in non-Western contexts. This paper presents findings from 27 semi-structured interviews with Chinese smart home product team members, including product/project managers, software/hardware engineers, user experience (UX) designers, legal/privacy experts, and marketers/operation specialists. We examine their privacy perspectives, practices, and risk mitigation strategies. Our results show that participants emphasized compliance with Chinese data privacy laws, which typically prioritized national security over individual privacy rights. China-specific cultural, social, and legal factors also influenced participants' ethical considerations and attitudes toward balancing user privacy and security with convenience. Drawing on our findings, we propose a set of recommendations for smart home product teams, along with socio-technical and legal interventions to address smart home privacy issues-especially those belonging to at-risk groups-in Chinese multi-user smart homes.
The growing use of smart home devices poses considerable privacy and security challenges, especially for individuals like migrant domestic workers (MDWs) who may be surveilled by their employers. This paper explores the privacy and security challenges experienced by MDWs in multi-user smart homes through in-depth semi-structured interviews with 26 MDWs and 5 staff members of agencies that recruit and/or train domestic workers in China. Our findings reveal that the relationships between MDWs, their employers, and agencies are characterized by significant power imbalances, influenced by Chinese cultural and social factors (such as Confucianism and collectivism), as well as legal ones. Furthermore, the widespread and normalized use of surveillance technologies in China, particularly in public spaces, exacerbates these power imbalances, reinforcing a sense of constant monitoring and control. Drawing on our findings, we provide recommendations to domestic worker agencies and policymakers to address the privacy and security challenges facing MDWs in Chinese smart homes.
Calls for engagement with the public in Artificial Intelligence (AI) research, development, and governance are increasing, leading to the use of surveys to capture people's values, perceptions, and experiences related to AI. In this paper, we critically examine the state of human participant surveys associated with these topics. Through both a reflexive analysis of a survey pilot spanning six countries and a systematic literature review of 44 papers featuring public surveys related to AI, we explore prominent perspectives and methodological nuances associated with surveys to date. We find that public surveys on AI topics are vulnerable to specific Western knowledge, values, and assumptions in their design, including in their positioning of ethical concepts and societal values, lack sufficient critical discourse surrounding deployment strategies, and demonstrate inconsistent forms of transparency in their reporting. Based on our findings, we distill provocations and heuristic questions for our community, to recognize the limitations of surveys for meeting the goals of engagement, and to cultivate shared principles to design, deploy, and interpret surveys cautiously and responsibly.
Large language models (LLMs) can provide users with false, inaccurate, or misleading information, and we consider the output of this type of information as what Natale (2021) calls `banal' deceptive behaviour. Here, we investigate peoples' perceptions of ChatGPT-generated deceptive behaviour and how this affects peoples' own behaviour and trust. To do this, we use a mixed-methods approach comprising of (i) an online survey with 220 participants and (ii) semi-structured interviews with 12 participants. Our results show that (i) the most common types of deceptive information encountered were over-simplifications and outdated information; (ii) humans' perceptions of trust and `worthiness' of talking to ChatGPT are impacted by `banal' deceptive behaviour; (iii) the perceived responsibility for deception is influenced by education level and the frequency of deceptive information; and (iv) users become more cautious after encountering deceptive information, but they come to trust the technology more when they identify advantages of using it. Our findings contribute to the understanding of human-AI interaction dynamics in the context of \textit{Deceptive AI Ecosystems}, and highlight the importance of user-centric approaches to mitigating the potential harms of deceptive AI technologies.
While the literature on permissions from the end-user perspective is rich, there is a lack of empirical research on why developers request permissions, their conceptualization of permissions, and how their perspectives compare with end-users' perspectives. Our study aims to address these gaps using a mixed-methods approach. Through interviews with 19 app developers and a survey of 309 Android and iOS end-users, we found that both groups shared similar concerns about unnecessary permissions breaking trust, damaging the app's reputation, and potentially allowing access to sensitive data. We also found that developer participants sometimes requested multiple permissions due to confusion about the scope of certain permissions or third-party library requirements. Additionally, most end-user participants believed they were responsible for granting a permission request, and it was their choice to do so, a belief shared by many developer participants. Our findings have implications for improving the permission ecosystem for both developers and end-users.
A cookie banner pops up when a user visits a website for the first time, requesting consent to the use of cookies and other trackers for a variety of purposes. Unlike prior work that has focused on evaluating the user interface (UI) design of cookie banners, this paper presents an in-depth analysis of what cookie banners say to users to get their consent. We took an interdisciplinary approach to determining what cookie banners should say. Following the legal requirements of the ePrivacy Directive (ePD) and the General Data Protection Regulation (GDPR), we manually annotated around 400 cookie banners presented on the most popular English-speaking websites visited by users residing in the EU. We focused on analyzing the purposes of cookie banners and how these purposes were expressed (e.g., any misleading or vague language, any use of jargon). We found that 89% of cookie banners violated applicable laws. In particular, 61% of banners violated the purpose specificity requirement by mentioning vague purposes, including "user experience enhancement". Further, 30% of banners used positive framing, breaching the freely given and informed consent requirements. Based on these findings, we provide recommendations that regulators can find useful. We also describe future research directions.
Nowadays, all major web browsers have a private browsing mode. However, the mode's benefits and limitations are not particularly understood. Through the use of survey studies, prior work has found that most users are either unaware of private browsing or do not use it. Further, those who do use private browsing generally have misconceptions about what protection it provides. However, prior work has not investigated \emph{why} users misunderstand the benefits and limitations of private browsing. In this work, we do so by designing and conducting a three-part study: (1) an analytical approach combining cognitive walkthrough and heuristic evaluation to inspect the user interface of private mode in different browsers; (2) a qualitative, interview-based study to explore users' mental models of private browsing and its security goals; (3) a participatory design study to investigate why existing browser disclosures, the in-browser explanations of private browsing mode, do not communicate the security goals of private browsing to users. Participants critiqued the browser disclosures of three web browsers: Brave, Firefox, and Google Chrome, and then designed new ones. We find that the user interface of private mode in different web browsers violates several well-established design guidelines and heuristics. Further, most participants had incorrect mental models of private browsing, influencing their understanding and usage of private mode. Additionally, we find that existing browser disclosures are not only vague, but also misleading. None of the three studied browser disclosures communicates or explains the primary security goal of private browsing. Drawing from the results of our user study, we extract a set of design recommendations that we encourage browser designers to validate, in order to design more effective and informative browser disclosures related to private mode.