AI companions are increasingly popular among teenagers, yet current platforms lack safeguards to address developmental risks and harmful normalization. Despite growing concerns, little is known about how parents and developmental psychology experts assess these interactions or what protections they consider necessary. We conducted 26 semi-structured interviews with parents and experts, who reviewed real-world youth–AI companion conversation snippets. We found that stakeholders assessed risks contextually, attending to factors such as youth maturity, AI character age, and how AI characters modeled values and norms. We also identified distinct logics of assessment: parents flagged single events, such as a mention of suicide or flirtation, as high risk, whereas experts looked for patterns over time, such as repeated references to self-harm or sustained dependence. Both groups proposed interventions, with parents favoring broader oversight and experts preferring cautious, crisis-only escalation paired with youth-facing safeguards. These findings provide directions for embedding safety into AI companion design.

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.

Figure 1: From left to right: (a) the original website, (b) Option 1 (Regenerated HTML), and (c) Option 2 (Reorganized HTML Tags).Option 1 is generated using a GenAI-powered extension that rewrites the HTML to enhance accessibility, while Option 2 reorganizes the original HTML tags to address accessibility issues without altering the visual design.This example highlights the changes made in both versions compared to the original website.Both versions simplify navigation by removing single-category headings, reducing clutter and minimizing fatigue for screen reader users.

Large Language Models (LLMs) are increasingly used by teenagers and young adults in everyday life, ranging from emotional support and creative expression to educational assistance. However, their unique vulnerabilities and risk profiles remain under-examined in current safety benchmarks and moderation systems, leaving this population disproportionately exposed to harm. In this work, we present Youth AI Risk (YAIR), the first benchmark dataset designed to evaluate and improve the safety of youth–LLM interactions. YAIR consists of 12,449 annotated conversation snippets spanning 78 fine-grained risk types, grounded in a taxonomy of youth-specific harms such as grooming, boundary violation, identity confusion, and emotional overreliance. We systematically evaluate widely adopted moderation models on YAIR and find that existing approaches substantially underperform in detecting youth-centered risks, often missing contextually subtle yet developmentally harmful interactions. To address these gaps, we introduce YouthSafe, a real-time risk detection model optimized for youth–GenAI contexts. YouthSafe significantly outperforms prior systems across multiple metrics on risk detection and classification, offering a concrete step toward safer and more developmentally appropriate AI interactions for young users.

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.

Across many domains (e.g., media/entertainment, mobile apps, finance, IoT, cybersecurity), there is a growing need for stateful analytics over streams of events to meet key business outcomes. Stateful analytics over event streams entails carefully modeling the sequence, timing, and contextual correlations of events to dynamic attributes. Unfortunately, existing frameworks and languages (e.g., SQL, Flink, Spark) entail significant code complexity and expert effort to express such stateful analytics because of their dynamic and stateful nature. Our overarching goal is to simplify and democratize stateful analytics. Through an iterative design and evaluation process including a foundational user study and two rounds of formative evaluations with 15 industry practitioners, we created SEAM-EZ, a no-code visual programming platform for quickly creating and validating stateful metrics. SEAM-EZ features a node-graph editor, interactive tooltips, embedded data views, and auto-suggestion features to facilitate the creation and validation of stateful analytics. We then conducted three real-world case studies of SEAM-EZ with 20 additional practitioners. Our results suggest that practitioners who previously could not or had to spend significant effort to create stateful metrics using traditional tools such as SQL or Spark can now easily and quickly create and validate such metrics using SEAM-EZ.

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.

Empathy for children is critical for designing AI technologies that may affect children. This paper presents the work in progress of a study on the feasibility of a new method to provide objective understanding of people’s empathy for children based on functional near infrared spectroscopy (fNIRS). Adult participants (n=13) were presented with benign or concerning scenarios involving children interacting with AI technologies. Their brain activation patterns were recorded and analyzed. Preliminary data analysis revealed distinctive patterns in the mPFC region, which justifies future work to fully realize the potential of this method.

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).

Today’s cybersecurity and AI technologies are often fraught with ethical challenges. One promising direction is to teach cybersecurity and AI ethics to today’s youth. However, we know little about how these subjects are taught before college. Drawing from interviews of US high school teachers (n=16) and students (n=11), we find that cybersecurity and AI ethics are often taught in non-technical classes such as social studies and language arts. We also identify relevant topics, of which epistemic norms, privacy, and digital citizenship appeared most often. While teachers leverage traditional and novel teaching strategies including discussions (treating current events as case studies), gamified activities, and content creation, many challenges remain. For example, teachers hesitate to discuss current events out of concern for appearing partisan and angering parents; cyber hygiene instruction appears very ineffective at educating youth and promoting safer online behavior; and generational differences make it difficult for teachers to connect with students. Based on the study results, we offer practical suggestions for educators, school administrators, and cybersecurity practitioners to improve youth education on cybersecurity and AI ethics.

Many people are concerned about how their personal data is used for online behavioral advertising (OBA). Ad targeting explanations have been proposed as a way to reduce this concern by improving transparency. However, it is unclear when and why people might want ad targeting explanations. Without this insight, we run the risk of designing explanations that do not address real concerns. To bridge this gap, we conducted a four-week, mixed-methods field study with 60 participants to understand when and why people want targeting explanations for the ads they actually encountered while browsing the web. We found that users wanted explanations for around 30% of the 4,251 ads we asked them about during the study, and that subjective perceptions of how their personal data was collected and shared were highly correlated with when users wanted ad explanations. Often, users wanted these explanations to confirm or deny their own preconceptions about how their data was collected or the motives of advertisers. A key upshot of our work is that one-size-fits-all approaches to ad explanations are likely to fail at addressing people’s lived concerns about ad targeting; instead, more personalized explanations are needed.

Due to the challenges to detect and filter phishing emails, it is inevitable that some phishing emails can still reach a user’s inbox. As a result, email providers such as Gmail have implemented phishing warnings to help users to better recognize phishing attempts. Existing research has primarily focused on phishing warnings for sighted users and yet it is not well understood how people with visual impairments interact with phishing emails and warnings. In this paper, we worked with a group of users (N=41) with visual impairments to study the effectiveness of existing warnings and explore more inclusive designs (using Gmail warning designs as a baseline for comparison). We took a multipronged approach including an exploratory study (to understand the challenges faced by users), user-in-the-loop design and prototyping, and the main study (to assess the impact of design choices). Our results show that users with visual impairments often miss existing Gmail warnings because the current design (e.g., warning position, HTML tags used) does not match well with screen reader users’ reading habits. The inconsistencies of the warnings (e.g., across the Standard and HTML view) also create obstacles to users. We show that an inclusive design (combining audio warning, shortcut key, and warning page overlay) can effectively increase the warning noticeability. Based on our results, we make a number of recommendations to email providers.

Home is a person's castle, a private and protected space. Internet-connected devices such as locks, cameras, and speakers might make a home "smarter" but also raise privacy issues because these devices may constantly and inconspicuously collect, infer or even share information about people in the home. To explore user-centered privacy designs for smart homes, we conducted a co-design study in which we worked closely with diverse groups of participants in creating new designs. This study helps fill the gap in the literature between studying users' privacy concerns and designing privacy tools only by experts. Our participants' privacy designs often relied on simple strategies, such as data localization, disconnection from the Internet, and a private mode. From these designs, we identified six key design factors: data transparency and control, security, safety, usability and user experience, system intelligence, and system modality. We discuss how these factors can guide design for smart home privacy.

As the Internet of Things (IoT) devices make their ways into people's homes, traditional dwellings are turning into smart homes. While prior empirical studies have examined people's privacy concerns of smart homes and their desired ways of mitigating these concerns, the focus was primarily on the end users or device owners. Our research investigated the privacy perceptions and design ideas of smart home bystanders, i.e., people who are not the owners nor the primary users of smart home devices but can potentially be involved in the device usage, such as other family members or guests. We conducted focus groups and co-design activities with eighteen participants. We identified three impacting factors of bystanders' privacy perceptions (e.g., perceived norms) and a number of design factors to mitigate their privacy concerns (e.g., asking for device control). We highlighted bystanders' needs for privacy and controls, as well as the tension of privacy expectations between the owners/users and the bystanders in smart homes. We discussed how future designs can better support and balance the privacy needs of different stakeholders in smart homes.

Abstract Smart home devices challenge a long-held notion that the home is a private and protected place. With this in mind, many developers market their products with a focus on privacy in order to gain user trust, yet privacy tensions arise with the growing adoption of these devices and the risk of inappropriate data practices in the smart home (e.g., secondary use of collected data). Therefore, it is important for developers to consider individual user preferences and how they would change under varying circumstances, in order to identify actionable steps towards developing user trust and exercising privacy-preserving data practices. To help achieve this, we present the design and evaluation of machine learning models that predict (1) personalized allow/deny decisions for different information flows involving various attributes, purposes, and devices (AUC .868), (2) what circumstances may change original decisions (AUC .899), and (3) how much (US dollars) one may be willing to pay or receive in exchange for smart home privacy (RMSE 12.459). We show how developers can use our models to derive actionable steps toward privacy-preserving data practices in the smart home.

Authentication is a mundane yet often integral part of people's experiences with computing devices and Internet services. Since most authentication mechanisms were designed without explicitly considering people with disabilities, these mechanisms may pose significant challenges for these users. In this paper, we report results from a contextual inquiry study on the authentication experiences and challenges of people with cognitive impairments. We identified a number of difficulties our participants experienced, such as remembering usernames and passwords, typing on the keyboard, logging out from their existing online accounts and being aware of errors. We hope the insights we found will indeed make future authentication systems more accessible and easier to use for users with cognitive impairments.

Drones pose privacy concerns such as surveillance and stalking. Many technology-based or policy-based mechanisms have been proposed to mitigate these concerns. However, it is unclear how drone controllers and bystanders perceive these mechanisms and whether people intend to adopt them. In this paper, we report results from two rounds of online survey with 169 drone controllers and 717 bystanders in the U.S. We identified respondents' perceived pros and cons of eight privacy mechanisms. We found that owner registration and automatic face blurring individually received most support from both controllers and bystanders. Our respondents also suggested using varied combinations of mechanisms under different drone usage scenarios, highlighting their context-dependent preferences. We outline a set of important questions for future privacy designs and public policies of drones.

Prior research has discovered various privacy concerns that bystanders have about drones. However, little is known about drone controllers' privacy perceptions and practices of drones. Understanding controllers' perspective is important because it will inform whether controllers' current practices protect or infringe on bystanders' privacy and what mechanisms could be designed to better address the potential privacy issues of drones. In this paper, we report results from interviews of 12 drone controllers in the US. Our interviewees treated safety as their top priority but considered privacy issues of drones exaggerated. Our results also highlight many significant differences in how controllers and bystanders think about drone privacy, for instance, how they determine public vs. private spaces and whether notice and consent of bystanders are needed.

Online Behavioral Advertising (OBA) is pervasive on the Internet. While there is a line of empirical research that studies Internet users' attitudes and privacy preferences of OBA, little is known about their actual understandings of how OBA works. This is an important question to answer because people often draw on their understanding to make decisions. Through a qualitative study conducted in an iterative manner, we identify four "folk models" held by our participants about how OBA works and show how these models are either incomplete or inaccurate in representing common OBA practices. We discuss how privacy tools can be designed to consider these folk models. In addition, most of our participants felt that the information being tracked is more important than who the web trackers are. This suggests the potential for an information-based blocking scheme rather than a tracker-based blocking scheme used by most existing ad-blocking tools.

The field of security and privacy has made steady progresses in developing technical mechanisms, which I refer to as the first wave of security and privacy research. Since the 70's, human factors and usability have been recognized as a key property of effective security and privacy mechanisms. This is what I call the second wave of security and privacy research, focusing on usability. In this article, I propose and advocate for a third wave of research that I call inclusive security and privacy, which is concerned with designing security and privacy mechanisms that are inclusive to people with various characteristics, abilities, needs and values. I present a preliminary research framework and research agenda for advancing inclusive security and privacy.

Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are widely used by organizations, researchers, and individuals to outsource a broad range of tasks to crowd workers. Prior research has shown that crowdsourcing can pose privacy risks (e.g., de-anonymization) to crowd workers. However, little is known about the specific privacy issues crowd workers have experienced and how they perceive the state of privacy in crowdsourcing. In this paper, we present results from an online survey of 435 MTurk crowd workers from the US, India, and other countries and areas. Our respondents reported different types of privacy concerns (e.g., data aggregation, profiling, scams), experiences of privacy losses (e.g., phishing, malware, stalking, targeted ads), and privacy expectations on MTurk (e.g., screening tasks). Respondents from multiple countries and areas reported experiences with the same privacy issues, suggesting that these problems may be endemic to the whole MTurk platform. We discuss challenges, high-level principles and concrete suggestions in protecting crowd workers'; privacy on MTurk and in crowdsourcing more broadly.

Online Behavioral Advertising (OBA), which involves tracking people's online behaviors, raises serious privacy concerns. We present results from a scenario-based online survey study on American and Chinese Internet users' privacy preferences of OBA. Since privacy is context-dependent, we investigated the effects of country (US vs. China), activity (e.g., online shopping vs. online banking), and platform (desktop/laptop vs. mobile app) on people's willingness to share their information for OBA. We found that American respondents were significantly less willing to share their data and had more specific concerns than their Chinese counterparts. We situate these differences in the broader historical, legal, and social scenes of these countries. We also found that respondents' OBA preferences varied significantly across different online activities, suggesting the potential of context-aware privacy tools for OBA. However, we did not find a significant effect of platform on people's OBA preferences. Lastly, we discuss design implications for privacy tools.

Visually impaired users face various challenges in web authentication. We designed UniPass, an accessible password manager for visually impaired users based on a smart device. To evaluate UniPass, we tested and compared UniPass with two commercial password managers: LastPass, a popular password manager and StrongPass, a smart device-based password manager. Our study results of ten users, six blind and four with low vision, suggest that password managers are a promising authentication approach for visually impaired users. Participants using UniPass had the highest task completion rate and took the shortest time to complete an authentication related task. Furthermore, the majority (seven out of ten) of our participants preferred UniPass over LastPass and StrongPass.

Abstract Drones are unmanned aircraft controlled remotely or operated autonomously. While the extant literature suggests that drones can in principle invade people’s privacy, little is known about how people actually think about drones. Drawing from a series of in-depth interviews conducted in the United States, we provide a novel and rich account of people’s privacy perceptions of drones for civilian uses both in general and under specific usage scenarios. Our informants raised both physical and information privacy issues against government, organization and individual use of drones. Informants’ reasoning about the acceptance of drone use was in part based on whether the drone is operating in a public or private space. However, our informants differed significantly in their definitions of public and private spaces. While our informants’ privacy concerns such as surveillance, data collection and sharing have been raised for other tracking technologies such as camera phones and closed-circuit television (CCTV), our interviews highlight two heightened issues of drones: (1) powerful yet inconspicuous data collection, (2) hidden and inaccessible drone controllers. These two aspects of drones render some of people’s existing privacy practices futile (e.g., notice recording and ask controllers to stop or delete the recording). Some informants demanded notifications of drones near them and expected drone controllers asking for their explicit permissions before recording. We discuss implications for future privacy-enhancing drone designs.

Effective emotion regulation can benefit many aspects of our lives such as mental health and work performance. Informed by emotion regulation theories and in consultation with our university counseling center, we designed a novel location-based mobile social app, Emotion Map, to help improve people's awareness and regulations of their emotions. The app allows users to log their emotions with the associated time, location, and activity information. Users can keep these logged emotions to themselves or share them with others publicly or anonymously. We conducted a 4-week field trial of the app with 14 university students. Combining usage logs and in-person interviews, our analysis shows promising results of the app. Specifically, we found that the app improved some participants' self-knowledge of their emotions, supported their various emotion regulations, and enabled better awareness of the emotion statuses of their friends and communities.

Current campus communication regarding safety-related issues can be improved for both efficiency and accessibility. We observed a unique opportunity to develop a mobile crowdsourcing system, which allows university community members to report safety related incidents to the campus police department and to share their reports with other users of the system. To better inform the design of such a system, we applied drift-diffusion models in cognitive psychology to model the effect of various factors on users' sharing tendency. We conducted a laboratory experiment with 30 participants. We also ran an MTurk study with 230 participants to explore the feature of anonymous sharing in the application design. In this paper we report various results, including the findings that the time of day, location, and type of crime each affects the likelihood and timeliness of sharing safety reports in several different ways. We also discuss the implications for design of mobile crowdsourcing systems for public safety in general.

Anecdotal evidence and scholarly research have shown that Internet users may regret some of their online disclosures. To help individuals avoid such regrets, we designed two modifications to the Facebook web interface that nudge users to consider the content and audience of their online disclosures more carefully. We implemented and evaluated these two nudges in a 6-week field trial with 28 Facebook users. We analyzed participants' interactions with the nudges, the content of their posts, and opinions collected through surveys. We found that reminders about the audience of posts can prevent unintended disclosures without major burden; however, introducing a time delay before publishing users' posts can be perceived as both beneficial and annoying. On balance, some participants found the nudges helpful while others found them unnecessary or overly intrusive. We discuss implications and challenges for designing and evaluating systems to assist users with online disclosures.

Much of the debate surrounding online behavioral advertising (OBA) has centered on how to provide users with notice and choice. An important element left unexplored is how advertising companies' privacy practices affect users' attitudes toward data sharing. We present the results of a 2,912-participant online study investigating how facets of privacy practices---data retention, access to collected data, and scope of use---affect users' willingness to allow the collection of behavioral data. We asked participants to visit a health website, explained OBA to them, and outlined policies governing data collection for OBA purposes. These policies varied by condition. We then asked participants about their willingness to permit the collection of 30 types of information. We identified classes of information that most participants would not share, as well as classes that nearly half of participants would share. More restrictive data-retention and scope-of-use policies increased participants' willingness to allow data collection. In contrast, whether the data was collected on a well-known site and whether users could review and modify their data had minimal impact. We discuss public policy implications and improvements to user interfaces to align with users' privacy preferences.

We present results of a 45-participant laboratory study investigating the usability of nine tools to limit online behavioral advertising (OBA). We interviewed participants about OBA and recorded their behavior and attitudes as they configured and used a privacy tool, such as a browser plugin that blocks requests to specific URLs, a tool that sets browser cookies indicating a user's preference to opt out of OBA, or the privacy settings built into a web browser. We found serious usability flaws in all tools we tested. Participants found many tools difficult to configure, and tools' default settings were often minimally protective. Ineffective communication, confusing interfaces, and a lack of feedback led many participants to conclude that a tool was blocking OBA when they had not properly configured it to do so. Without being familiar with many advertising companies and tracking technologies, it was difficult for participants to use the tools effectively.

We report results of 48 semi-structured interviews about online behavioral advertising (OBA). We investigated non-technical users' attitudes about and understanding of OBA, using participants' expectations and beliefs to explain their attitudes. Participants found OBA to be simultaneously useful and privacy invasive. They were surprised to learn that browsing history is currently used to tailor advertisements, yet they were aware of contextual targeting.

We investigate regrets associated with users' posts on a popular social networking site. Our findings are based on a series of interviews, user diaries, and online surveys involving 569 American Facebook users. Their regrets revolved around sensitive topics, content with strong sentiment, lies, and secrets. Our research reveals several possible causes of why users make posts that they later regret: (1) they want to be perceived in favorable ways, (2) they do not think about their reason for posting or the consequences of their posts, (3) they misjudge the culture and norms within their social circles, (4) they are in a "hot" state of high emotion when posting, or under the influence of drugs or alcohol, (5) their postings are seen by an unintended audience, (6) they do not foresee how their posts could be perceived by people within their intended audience, and (7) they misunderstand or misuse the Facebook platform. Some reported incidents had serious repercussions, such as breaking up relationships or job losses. We discuss methodological considerations in studying negative experiences associated with social networking posts, as well as ways of helping users of social networking sites avoid such regrets.

Secure Device Pairing is the bootstrapping of secure communication between two previously unassociated devices over a wireless channel. The human-imperceptible nature of wireless communication, lack of any prior security context, and absence of a common trust infrastructure open the door for Man-in-the-Middle (aka Evil Twin) attacks. A number of methods have been proposed to mitigate these attacks, each requiring user assistance in authenticating information exchanged over the wireless channel via some human-perceptible auxiliary channels, e.g., visual, acoustic or tactile.

What happens when the domains of HCI design and money intersect? This paper presents analyses from an ethnographic study of virtual currency use in China to discuss implications for game design, and HCI design more broadly. We found that how virtual currency is perceived, obtained, and spent can critically shape gamers' behavior and experience. Virtual and real currencies can interact in complex ways that promote, extend, and/or interfere with the value and character of game worlds. Bringing money into HCI design heightens existing issues of realness, trust, and fairness, and thus presents new challenges and opportunities for user experience innovation.

We analyze online gaming as a site of collaboration in a digital-physical hybrid. We ground our analysis in findings from an ethnographic study of the online game World of Warcraft in China. We examine the interplay of collaborative practices across the physical environment of China's Internet cafes and the virtual game space of World of Warcraft. Our findings suggest that it may be fruitful to broaden existing notions of physical-digital hybridity by considering the nuanced interplay between the digital and physical as a multi-dimensional environment or "ecology". We illustrate how socio-economics, government regulations and cultural value systems shaped a hybrid cultural ecology of online gaming in China.

The rising popularity of mobile applications and devices has brought about an enhanced interest in infrastructure support for mobile computing. Our work focuses on the development of a mobile grid infrastructure called MAPGrid (mobile applications on Grids), where grid resources are exploited as proxies to enable advanced mobile applications. However, intermittent availability of grid resources presents challenges for data-intensive mobile applications. In this paper, we propose novel methodologies for placing mobile data on grid proxies. We introduce a notion of two-tier architecture for MAPGrid, where the upper tier captures grid related features and the lower tier represents features associated with mobile environments. We further develop an intelligent mobile data placement mechanism that effectively balances tradeoffs between replication cost and data access cost by leveraging knowledge of grid availability and mobile data request patterns. Through extensive experimentation, we illustrate the superiority of the proposed techniques over several popular data placement strategies.

Software product line architectures (PLAs) have been widely recognized as a successful approach in industrial software development for improving productivity, software quality and time-to-market. In this paper, we focus on the usage of a PLA for a quite different purpose, namely, handling privacy constraints in Web personalization. To provide personalized services such as customized recommendations, a personalized Website collects users' personal data, which raises various privacy concerns. We aim at reconciling the benefits of web personalization with privacy constraints that come from users themselves as well as from privacy legislations and regulations that apply to a given user. We propose a dynamic, privacy-enabling personalization infrastructure and conceive it as a PLA. This infrastructure allows for dynamically selecting and instantiating personalization architectures that provide personalized services to each individual user and comply with the prevailing privacy constraints.