We propose a novel task, hierarchical instance tracking, which entails tracking all instances of predefined categories of objects and parts, while maintaining their hierarchical relationships. We introduce the first benchmark dataset supporting this task, consisting of 2,765 unique entities that are tracked in 552 videos and belong to 40 categories (across objects and parts). Evaluation of seven variants of four models tailored to our novel task reveals the new dataset is challenging. Our dataset is available at https://vizwiz.org/tasks-and-datasets/hierarchical-instance-tracking/

Insights into the unique security and privacy practices, risks, and solutions for people with disabilities are currently fragmented across disciplines.In this work, we present a literature review of 33 papers published at leading human-computer interaction, accessibility, and usable security and privacy venues.We categorize the contributions of these papers-ranging from interventions to empirical studies of risks and behaviors-and identify key themes and implications.Papers in this corpus highlight 1) the opportunities and risks of the data collected by assistive technologies and security and privacy tools, 2) the inaccessibility or low usability of security and privacy solutions for people with disabilities, and 3) the utility of customized, contextual security and privacy solutions.We conclude with best practices for collecting data from disabled communities and implications for the design of assistive technologies and security/privacy tools.

Blind and low vision (BLV) individuals use Generative AI (GenAI) tools to interpret and manage visual content in their daily lives.While such tools can enhance the accessibility of visual content and enable greater user independence, they also introduce complex challenges around visual privacy.In this paper, we investigate the current practices and future design preferences of blind and low vision individuals through an interview study with 21 participants.Our findings reveal a range of current practices with GenAI that balance privacy, efficiency, and emotional agency, with users accounting for privacy risks across six key scenarios: selfpresentation, indoor spatial privacy, outdoor spatial privacy, social media sharing, sharing with employer or professional setup, and handling professional content as employers.Our findings reveal design preferences, including on-device processing, zero-retention guarantees, sensitive content redaction, privacy-aware appearance indicators, and multimodal tactile mirrored interaction methods.We conclude with actionable design recommendations to support user-centered visual privacy through GenAI, expanding the notion of privacy and responsible handling of others' information.

Cryptocurrency practices worldwide are seen as innovative, yet they navigate a fragmented regulatory landscape across different countries. Many national authorities aim to balance promoting innovation, safeguarding consumers, and managing potential threats. In particular, it is unclear how people deal with cryptocurrencies in the countries where trading or mining is prohibited. This insight is crucial in conveying the risk reduction strategies. To address this, we conducted semi-structured interviews with 28 cryptocurrency traders and miners from Bangladesh, where the environment is hostile towards cryptocurrencies. Our research revealed that the participants use unique strategies to mitigate risks around cryptocurrencies. Our findings indicate a prevalent uncertainty at both personal and organizational levels concerning the interpretation of laws, a situation worsened by the actions of the major financial service providers who indirectly facilitate cryptocurrency transactions. We further connect our findings to the broader issues in HCI regarding folk models, informal market and legality, and education and awareness.

Stablecoins, a type of cryptocurrency pegged to another asset to maintain a stable price, have become an important part of the cryptocurrency ecosystem. Prior studies have primarily focused on examining the security of stablecoins from technical and theoretical perspectives, with limited investigation into users' risk perceptions and security behaviors in stablecoin practices. To address this research gap, we conducted a mixed-method study that included constructing a stablecoin interaction framework based on the literature, which informed the design of our interview protocol, semi-structured interviews (n=21), and Reddit data analysis (9,326 posts). We found that participants see stable value and regulatory compliance as key security advantages of stablecoins over other cryptocurrencies. However, participants also raised concerns about centralization risks in fiat-backed stablecoins, perceived challenges in crypto-backed stablecoins due to limited reliance on fully automated execution, and confusion regarding the complex mechanisms of algorithmic stablecoins. We proposed improving user education and optimizing mechanisms to address these concerns and promote the safer use of stablecoins.

The widespread use of Generative Artificial Intelligence (GAI) among teenagers has led to significant misuse and safety concerns. To identify risks and understand parental controls challenges, we conducted a content analysis on Reddit and interviewed 20 participants (seven teenagers and 13 parents). Our study reveals a significant gap in parental awareness of the extensive ways children use GAI, such as interacting with character-based chatbots for emotional support or engaging in virtual relationships. Parents and children report differing perceptions of risks associated with GAI. Parents primarily express concerns about data collection, misinformation, and exposure to inappropriate content. In contrast, teenagers are more concerned about becoming addicted to virtual relationships with GAI, the potential misuse of GAI to spread harmful content in social groups, and the invasion of privacy due to unauthorized use of their personal data in GAI applications. The absence of parental control features on GAI platforms forces parents to rely on system-built controls, manually check histories, share accounts, and engage in active mediation. Despite these efforts, parents struggle to grasp the full spectrum of GAI-related risks and to perform effective real-time monitoring, mediation, and education. We provide design recommendations to improve parent-child communication and enhance the safety of GAI use.

Individuals who are blind or have low vision (BLV) are at a heightened risk of sharing private information if they share photographs they have taken. To facilitate developing technologies that can help them preserve privacy, we introduce BIV-Priv-Seg, the first localization dataset originating from people with visual impairments that shows private content. It contains 1,028 images with segmentation annotations for 16 private object categories. We first characterize BIV-Priv-Seg and then evaluate modern models' performance for locating private content in the dataset. We find modern models struggle most with locating private objects that are not salient, small, and lack text as well as recognizing when private content is absent from an image. We facilitate future extensions by sharing our new dataset with the evaluation server at https://vizwiz.org/tasks-and-datasets/object-localization/

Social media platforms are an effective channel for businesses to reach potential audiences through targeted advertising. As the user base of these platforms expands and diversifies, research on targeted advertising and social media needs to go beyond well-studied Western contexts. In an online survey (n=412), we compared users' privacy-related perceptions and behaviors regarding targeted ads on social media in the United States (as a baseline representing Western contexts) and three South Asian countries: Bangladesh, India, and Pakistan. We found that participants in the US perceived significantly fewer benefits and more concerns related to security and privacy about targeted ads than those in the three South Asian countries. We also identified that individual's cultural values and religious affiliations influenced the observed cross-country variances. For instance, US participants identified less with vertical collectivism and vertical individualism than South Asian participants; these two cultural dimensions were, in turn, positively associated with perceived benefits. Our findings highlight the limitation of using one's country as a proxy for culture, as our findings show users' privacy perceptions regarding targeted advertising on social media are more fundamentally associated with their cultural values and religion. We discuss the corresponding design, education, and regulatory implications for targeted advertising on social media.

Key management has long remained a difficult unsolved problem in the field of usable security. While password-based key derivation functions (PBKDFs) are widely used to solve this problem in centralized applications, their low entropy and lack of a recovery mechanism make them unsuitable for use in decentralized contexts. The multi-factor key derivation function (MFKDF) is a recently proposed cryptographic primitive that aims to address these deficiencies by incorporating commonly used authentication factors into the key derivation process. In this paper, we implement an MFKDF-based Ethereum wallet and perform a user study with 27 participants to directly compare its usability against traditional cryptocurrency wallet architectures. Our results show that MFKDF-based applications outperform conventional key management approaches on both subjective and objective metrics, with a 37% higher average SUS score (p < 0.0001) and 71% faster task completion times (p < 0.0001) for the MFKDF-based wallet.

The advent of Web3 technologies promises unprecedented levels of user control and autonomy. However, this decentralization shifts the burden of security onto the users, making it crucial to understand their security behaviors and perceptions. To address this, our study introduces a comprehensive framework that identifies four core components of user interaction within the Web3 ecosystem: blockchain infrastructures, Web3-based Decentralized Applications (DApps), online communities, and off-chain cryptocurrency platforms. We delve into the security concerns perceived by users in each of these components and analyze the mitigation strategies they employ, ranging from risk assessment and aversion to diversification and acceptance. We further discuss the landscape of both technical and human-induced security risks in the Web3 ecosystem, identify the unique security differences between Web2 and Web3, and highlight key challenges that render users vulnerable, to provide implications for security design in Web3.

Cryptocurrency wallets come in various forms, each with unique usability and security features. Through interviews with 24 users, we explore reasons for selecting wallets in different contexts. Participants opt for smart contract wallets to simplify key management, leveraging social interactions. However, they prefer personal devices over individuals as guardians to avoid social cybersecurity concerns in managing guardian relationships. When engaging in high-stakes or complex transactions, they often choose browser-based wallets, leveraging third-party security extensions. For simpler transactions, they prefer the convenience of mobile wallets. Many participants avoid hardware wallets due to usability issues and security concerns with respect to key recovery service provided by manufacturer and phishing attacks. Social networks play a dual role: participants seek security advice from friends, but also express security concerns in soliciting this help. We offer novel insights into how and why users adopt specific wallets. We also discuss design recommendations for future wallet technologies based on our findings.

Blind individuals commonly share photos in everyday life. Despite substantial interest from the blind community in being able to independently obfuscate private information in photos, existing tools are designed without their inputs. In this study, we prototyped a preliminary screen reader-accessible obfuscation interface to probe for feedback and design insights. We implemented a version of the prototype through off-the-shelf AI models (e.g., SAM, BLIP2, ChatGPT) and a Wizard-of-Oz version that provides human-authored guidance. Through a user study with 12 blind participants who obfuscated diverse private photos using the prototype, we uncovered how they understood and approached visual private content manipulation, how they reacted to frictions such as inaccuracy with existing AI models and cognitive load, and how they envisioned such tools to be better designed to support their needs (e.g., guidelines for describing visual obfuscation effects, co-creative interaction design that respects blind users’ agency).

Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of (pseudonymous) entities toward a shared vision (e.g., promoting sustainability). In just a few years, over 4,000 DAOs have been launched in various domains, such as investment, education, health, and research. Despite such rapid growth and diversity, it is unclear how these DAOs actually work in practice. Given this, we aim to unpack how (well) DAOs work in practice. We conducted an in-depth analysis of a diverse set of 10 DAOs of various categories and smart contracts, leveraging on-chain data and interviewing DAO members. Specifically, we define metrics to characterize key aspects of DAOs, such as the degrees of decentralization and autonomy. We observed some DAOs having poor decentralization in voting, while decentralization has improved over time for one-person-one-vote DAOs. Lastly, we offer a set of design implications for future DAOs based on our findings.

While people’s perceptions of targeted ads have been studied extensively from a Western perspective (e.g., North America, Europe), we know little about users’ perceptions in the South Asian region. We interviewed 40 participants from two South Asian countries, Bangladesh and India, to explore their perceptions and practices regarding targeted ads on social media platforms. Participants identified emerging ad types, such as influencer-based ads and soft ads, through articles. In addition, participants often outweighed discounts over product quality when viewing ads. We also observed novel user mental models of targeted ads based on mobile app permissions and excessive AI usage. Participants often preferred ad control over transparency. While most participants rarely used ad settings, some controlled ads by changing mobile app permissions or muting ads on social media platforms. Participants also raised concerns about fraudulent targeted ads and privacy violations due to device sharing. We present potential design ideas to mitigate these concerns.

We present the design and creation of a disability-first dataset, “BIV-Priv,” which contains 728 images and 728 videos of 14 private categories captured by 26 blind participants to support downstream development of artificial intelligence (AI) models. While best practices in dataset creation typically attempt to eliminate private content, some applications require such content for model development. We describe our approach in creating this dataset with private content in an ethical way, including using props rather than participants’ own private objects and balancing multi-disciplinary perspectives (e.g., accessibility, privacy, computer vision) to meet the tangible metrics (e.g., diversity, category, amount of content) to support AI innovations. We observed challenges that our participants encountered during the data collection, including accessibility issues (e.g., understanding foreground vs. background object placement) and issues due to the sensitive nature of the content (e.g., discomfort in capturing some props such as condoms around family members).

A large number of mobile phone applications have been built and deployed to combat COVID-19, offering various services to users, including virus information, contact tracing, and symptom monitoring among others. At the same time, the privacy and security vulnerabilities of user data over these apps have become a big concern in many places. To examine this issue, we conducted a mixed-method study with a combined approach of app analysis and an online survey to understand the privacy vulnerabilities of such apps and get an overview of user perceptions around this issue. In addition, we considered the notion of privacy in two different socio-economic contexts (Global North and Global South) to specify similarities and differences in app-specific privacy functionalities (data practices, functional requirements, regulations, etc.) and identify factors that impacted users’ decision to use such apps (such as trust, preferences, concerns, motivations, etc.). Thus, this paper presents two diverse sets of opinions from these two geographic regions (including 27 countries), which provide a broader understanding of how the privacy concerns around COVID-19 are connected to various economic, political, and social factors. Furthermore, our analysis of 39 apps provides a deep insight into what many COVID-19 apps are lacking to ensure proper privacy practices and how those issues are entangled with various contextual challenges.

Mobile apps have transformed many aspects of clinical practice and are becoming a commonplace in healthcare settings. The recent COVID-19 pandemic has provided the opportunity for such apps to play an important role in reducing the spread of the virus. Several types of COVID-19 apps have enabled healthcare professionals and governments to communicate with the public regarding the pandemic spread, coronavirus awareness, and self-quarantine measures. While these apps provide immense benefits for the containment of the spread, privacy and security of these digital tracing apps are at the center of public debate. To address this gap, we conducted an online survey of a midwestern region in the United State to assess people’s attitudes toward such apps and to examine their privacy and security concerns and preferences. Survey results from 1,550 participants indicate that privacy/security protections and trust play a vital role in people’s adoption of such apps. Furthermore, results reflect users’ preferences wanting to have control over their personal information and transparency on how their data is handled. In addition, personal data protection priorities selected by the participants were surprising and yet revealing of the disconnect between technologists and users. In this article, we present our detailed survey results as well as design guidelines for app developers to develop innovative human-centered technologies that are not only functional but also respectful of social norms and protections of civil liberties. Our study examines users’ preferences for COVID-19 apps and integrates important factors of trust, willingness, and preferences in the context of app development. Through our research findings, we suggest mechanisms for designing inclusive apps’ privacy and security measures that can be put into practice for healthcare-related apps, so that timely adoption is made possible.