Traditional estate planning practices enable people to provide their heirs access to the assets left behind but are often insufficient for the transfer and management of online accounts. To understand how estate planning practices could be improved, we conducted 21 semi-structured interviews with older adults in the United States that explored their practices, concerns, and needs regarding postmortem online account access and management. We encountered few formalized digital estate planning practices; many participants use their credential management practices—primarily pen-and-paper—to provide postmortem account access. How participants envision account transfer is motivated by trust in their current practices and in their heirs, while concerns regarding technology hinder adoption of new methods. Participants consistently prioritize accounts with financial assets, and expectations surrounding postmortem account management vary based on individual circumstances, with the common goal of reducing burdens on executors and heirs. Our results suggest the need for developing technical standardization and expert guidance for digital estate planning.

To keep users safe online, current protections frequently employ blocklists of known malware and phishing websites. However, such defenses suffer from an inherent gap between malicious content creation and its detection, leaving a window where users are left vulnerable. To address this limitation, earlier research has shown that one could use individual user web browsing behavior to identify imminent exposure to malicious content. While existing methods frequently rely on temporal proximity (e.g., aggregating browsing patterns over the recent past), they do not leverage temporal ordering in user browsing, which results in suboptimal performance and is, in practice, inadequate given the low base rates of malware incidence. We introduce network and browser-level features (e.g., page rank, tab browsing time) and a temporal model that captures user behavior through a time-series representation. This not only improves classification performance by a significant margin (between 93% and 145% F1-score improvements) over previous models, but also maintains strong robustness across completely disparate sets of users. More importantly, our method shows strong resilience to concept drift, as performance holds steady over multiple years of testing. We discuss how this method is capable of anticipating future exposure. We also assess the relative importance of each feature to the performance, as well as their impact on false positive rates—whose minimization is critical to foster adoption. Finally, we discuss use cases for such behavior-based models.

People often use social media platforms to seek advice about scams like ecommerce fraud or phishing; however, little research has investigated the nature of such discussion. We conducted a multi-stage thematic analysis of 1,525 posts made to four communities focused on scam discussion on Reddit, primarily from /r/Scams. We found that posters use Reddit to identify scams, discuss the strategies employed by scammers, and obtain advice on coping with victimization. The scams discussed are primarily mediated by the internet or related technologies. Users in the communities we studied especially provide informational support and reassurance to victims, although some comments reinforce victim-blaming attitudes. We also observed qualitative differences in the types of support sought and given based on the community, with the board /r/Sextortion especially being used for emotional support. We conclude that Reddit's scam discussion communities serve as a valuable resource for scam prevention and remediation. Additionally, we discuss the potential for future research and law enforcement engagement on Reddit.

Most common forms of web tracking fail to maintain the continuity of a user's identity over long periods of time: cookies get deleted, IP addresses are reassigned, attributes used for browser fingerprinting change. These identity discontinuities help prevent adversaries from conducting persistent long-term tracking. In fact, many privacy-enhancing technologies (e.g., automatic cookie deletion, use of proxy servers, fingerprint obfuscation) are predicated on the ability of identity discontinuities to disrupt an adversary's tracking capability. While only evaluated on a limited scale, behavioral fingerprinting—identifying users based on habitual patterns in their web browsing—may provide adversaries the key to linking users' identities across these discontinuities. To assess this potential threat, we provide an analysis of behavioral fingerprinting at scale, with over 150,000 users across two years, and the first assessment of the impact of these techniques on user anonymity online. Overall, we find that behavioral fingerprints are relatively unique, with most browsing sessions retaining little to no anonymity even at scale. Furthermore, users' behavioral fingerprints are consistent, evolving slowly over the course of months to years. Together, these findings satisfy the preconditions for effective identity linking. We go on to demonstrate that optimal performance is achieved when an adversary can observe 15–25 browsing sessions prior to a discontinuity and 10–15 sessions after. However, an adversary can eliminate 84–95% of a user's anonymity having observed just a single session pre- and post-discontinuity. After a discontinuity occurs, a user loses an average of 78–85% of their anonymity within the first 60 seconds of browsing and 90% of their anonymity within the first 10 minutes—largely negating the anonymity gains of privacy protections that induce discontinuities. We find that visiting fewer web pages, diversifying the websites visited, and avoiding niche content can help a user's browsing remain anonymous. Finally, we demonstrate that the combination of behavioral and browser fingerprinting can outperform each method individually, achieving an F1 score of 0.869 across 100,000 users.

The U.S. Government is developing a package label to help consumers access reliable security and privacy information about Internet of Things (IoT) devices when making purchase decisions. The label will include the U.S. Cyber Trust Mark, a QR code to scan for more details, and potentially additional information. To examine how label information complexity and educational interventions affect comprehension of security and privacy attributes and label QR code use, we conducted an online survey with 518 IoT purchasers. We examined participants’ comprehension and preferences for three labels of varying complexities, with and without an educational intervention. Participants favored and correctly utilized the two higher-complexity labels, showing a special interest in the privacy-relevant content. Furthermore, while the educational intervention improved understanding of the QR code’s purpose, it had a modest effect on QR scanning behavior. We highlight clear design and policy directions for creating and deploying IoT security and privacy labels.

As energy infrastructure becomes more interconnected, understanding cybersecurity risks to production systems requires integrating operational and computer security knowledge. We interviewed 18 experts working in the field of energy critical infrastructure to compare what information they find necessary to assess the impact of computer vulnerabilities on energy operational technology. These experts came from two groups: 1) computer security experts and 2) energy sector operations experts. We find that both groups responded similarly for general categories of information and displayed knowledge about both domains, perhaps due to their interdisciplinary work at the same organization. Yet, we found notable differences in the details of their responses and in their stated perceptions of each group’s approaches to impact assessment. Their suggestions for collaboration across domains highlighted how these two groups can work together to help each other secure the energy grid. Our findings inform the development of interdisciplinary security approaches in critical-infrastructure contexts.

Digital financial services have catalyzed financial inclusion in Africa. Commonly implemented as a mobile wallet service referred to as mobile money (MoMo), the technology provides enormous benefits to its users, some of whom have long been unbanked. While the benefits of mobile money services have largely been documented, the challenges that arise—especially in the interactions between human stakeholders—remain relatively unexplored. In this study, we investigate the practices of mobile money users in their interactions with mobile money agents. We conduct 72 structured interviews in Kenya and Tanzania (n=36 per country). The results show that users and agents design workarounds in response to limitations and challenges that users face within the ecosystem. These include advances or loans from agents, relying on the user-agent relationships in place of legal identification requirements, and altering the intended transaction execution to improve convenience. Overall, the workarounds modify one or more of what we see as the core components of mobile money: the user, the agent, and the transaction itself. The workarounds pose new risks and challenges for users and the overall ecosystem. The results suggest a need for rethinking privacy and security of various components of the ecosystem, as well as policy and regulatory controls to safeguard interactions while ensuring the usability of mobile money.

Online dating services present significant privacy risks, especially for LGBTQ+ people who are "in the closet" and have not shared their LGBTQ+ identity with others. We conducted a survey (n = 114) and nine follow-up interviews with US-based, closeted users of online dating services focused on their privacy experience. We found that participants in the study were strongly concerned about the risk of being seen by social relations and institutional data sharing practices like targeted advertising. Participants experienced a range of privacy and safety harms, including inadvertent outing, unauthorized saving and sharing of photos, extortion, and harassment. To protect their privacy, participants typically limited the amount of information and the photos they included in their profile. In order to improve their privacy experience, participants requested better profile visibility controls, limits on the ability of others to download or screenshot their photos, better user verification, and making premium privacy features available for free.

Users of online services often encounter cookie banners that ask them to consent to different categories of cookies. Frequently, these categories are labelled using the four categories defined by the 2012 Cookie Guide from the UK's International Chamber of Commerce (ICC). However, prior research suggests that users have difficulty understanding what these category labels actually mean. We conducted a four-part study to identify labels that more intuitively convey the four cookie categories. First, we crowd sourced new category labels. We then evaluated users' comprehension and sentiment towards the labels in a series of surveys focused on definitions and hypothetical scenarios. Finally, we selected a new slate of category labels based on the results of the prior surveys, and conducted a between-subjects, online behavioral experiment to compare the new slate with the original labels. We ultimately recommend that the industry adopt the category label ``anonymous analytics cookies'' in lieu of the term ``performance cookies'' and ``extra functionality cookies'' instead of ``functional cookies.'' Adopting our recommended terms would both improve the usability of current cookie consent interfaces and any future privacy consent mechanisms that use the same categorization. We also recommend revisiting the categories themselves as the distinctions between these categories do not seem to be well understood and may not reflect useful distinctions for privacy decision making.

Privacy labels---standardized, compact representations of data collection and data use practices---are often presented as a solution to the shortcomings of privacy policies. Apple introduced mandatory privacy labels for apps in its App Store in December 2020; Google introduced mandatory labels for Android apps in July 2022. iOS app privacy labels have been evaluated and critiqued in prior work. In this work, we evaluated Android Data Safety Labels and explored how differences between the two label designs impact user comprehension and label utility. We conducted a between-subjects, semi-structured interview study with 12 Android users and 12 iOS users. While some users found Android Data Safety Labels informative and helpful, other users found them too vague. Compared to iOS App Privacy Labels, Android users found the distinction between data collection groups more intuitive and found explicit inclusion of omitted data collection groups more salient. However, some users expressed skepticism regarding elided information about collected data type categories. Most users missed critical information due to not expanding the accordion interface, and they were surprised by collection practices excluded from Android's definitions. Our findings also revealed that Android users generally appreciated information about security practices included in the labels, and iOS users wanted that information added.

When companies release marketing materials aimed at promoting their privacy practices or highlighting specific privacy features, what do they actually communicate to consumers? In this paper, we explore the impact of privacy marketing on: (1) consumers’ attitudes toward the organizations providing the campaigns, (2) overall privacy awareness, and (3) the actionability of suggested privacy advice. To this end, we investigated the impact of four privacy advertising videos and one privacy game published by five different technology companies. We conducted 24 semi-structured interviews with participants randomly assigned to view one or two of the videos or play the game. Our findings suggest that awareness of privacy features can contribute to positive perceptions of a company or its products. The ads we tested were more successful in communicating the advertised privacy features than the game we tested. We observed that advertising a single privacy feature using a single metaphor in a short ad increased awareness of the advertised feature. The game failed to communicate privacy features or motivate study participants to use the features. Our results also suggest that privacy campaigns can be useful for raising awareness about privacy features and improving brand image, but may not be the most effective way to teach viewers how to use privacy features.

Websites implement cookie consent interfaces to obtain users’ permission to use non-essential cookies, as required by privacy regulations. We extend prior research evaluating the impact of interface design on cookie consent through an online behavioral experiment (n = 1359) in which we prompted mobile and desktop users from the UK and US to make cookie consent decisions using one of 14 interfaces implemented with the OneTrust consent management platform (CMP). We found significant effects on user behavior and sentiment for multiple explanatory variables, including more negative sentiment towards the consent process among UK participants and lower comprehension of interface information among mobile users. The design factor that had the largest effect on user behavior was the initial set of options displayed in the cookie banner. In addition to providing more evidence of the inadequacy of current cookie consent processes, our results have implications for website operators and CMPs.

Tech companies that rely on ads for business argue that users have control over their data via ad privacy settings. However, these ad settings are often hidden. This work aims to inform the design of findable ad controls and study their impact on users’ behavior and sentiment. We iteratively designed ad control interfaces that varied in the setting’s (1) entry point (within ads, at the feed’s top) and (2) level of actionability, with high actionability directly surfacing links to specific advertisement settings, and low actionability pointing to general settings pages (which is reminiscent of companies’ current approach to ad controls). We built a Chrome extension that augments Facebook with our experimental ad control interfaces and conducted a between-subjects online experiment with 110 participants. Results showed that entry points within ads or at the feed’s top, and high actionability interfaces, both increased Facebook ad settings’ findability and discoverability, as well as participants’ perceived usability of them. High actionability also reduced users’ effort in finding ad settings. Participants perceived high and low actionability as equally usable, which shows it is possible to design more actionable ad controls without overwhelming users. We conclude by emphasizing the importance of regulation to provide specific and research-informed requirements to companies on how to design usable ad controls.

Privacy scholars have long studied, and argued about, a so-called privacy paradox---the alleged gap between individuals' claims of caring about privacy and their actual behaviors. This manuscript explores whether a different type of mismatch occurs in an online sample of US participants: a mismatch between participants' dismissive perspectives on privacy and their privacy-protective behaviors. In a series of online studies with Prolific US participants we tackle two research questions: is there evidence of mismatches between (dismissive) privacy perspectives, and (protective) privacy behaviors? If so, what can explain those mismatches? In a Behavior Elicitation study, we collect a corpus of privacy-regulating and privacy-protective behaviors. Next, in Study 1, we find evidence that engagement in a broad array of privacy behaviors is, in fact, very common in our sample. We also find that mismatches between dismissive privacy perspectives and protective behaviors emerge in a large proportion of participants. Finally, in Study 2, we uncover several common but distinct reasons for those mismatches, including construing seemingly protective behaviors as motivated by reasons other than privacy, and nuanced stances on when to express privacy concern. Collectively, the results indicate that individuals who are seemingly dismissive of privacy concerns engage in behaviors that can be construed as privacy-seeking. The findings highlight the nuances of individual privacy decision-making and suggest that public policy related to privacy should account for the evidence for widespread privacy-seeking behaviors.

As technology companies develop mass market augmented reality (AR) glasses that are increasingly sensor-laden and affordable, uses of such devices pose potential privacy and security problems. Though prior work has broadly addressed some of these problems, our work specifically addresses the potential data collection of 15 data types by AR glasses and five potential data uses. Via semi-structured interviews, we explored the attitudes and concerns of 21 current AR technology users regarding potential data collection and data use by hypothetical consumer-grade AR glasses. Participants expressed diverse concerns and suggested potential limits to AR data collection and use, evoking privacy concepts and informational norms. We discuss how participants’ attitudes and reservations about data collection and use, like definitions of privacy, are varying and context-dependent, and make recommendations for designers and policy makers, including customizable and multidimensional privacy solutions.

Many websites have added cookie consent interfaces to meet regulatory consent requirements. While prior work has demonstrated that they often use dark patterns — design techniques that lead users to less privacy-protective options — other usability aspects of these interfaces have been less explored. This study contributes a comprehensive, two-stage usability assessment of cookie consent interfaces. We first inspected 191 consent interfaces against five dark pattern heuristics and identified design choices that may impact usability. We then conducted a 1,109-participant online between-subjects experiment exploring the usability impact of seven design parameters. Participants were exposed to one of 12 consent interface variants during a shopping task on a prototype e-commerce website and answered a survey about their experience. Our findings suggest that a fully-blocking consent interface with in-line cookie options accompanied by a persistent button enabling users to later change their consent decision best meets several design objectives.

Apple announced the introduction of app privacy details to their App Store in December 2020, marking the first ever real-world, large-scale deployment of the privacy nutrition label concept, which had been introduced by researchers over a decade earlier. The Apple labels are created by app developers, who self-report their app’s data practices. In this paper, we present the first study examining the usability and understandability of Apple’s privacy nutrition label creation process from the developer’s perspective. By observing and interviewing 12 iOS app developers about how they created the privacy label for a real-world app that they developed, we identified common challenges for correctly and efficiently creating privacy labels. We discuss design implications both for improving Apple’s privacy label design and for future deployment of other standardized privacy notices.

We conducted an online survey and remote usability study to explore user needs related to advertising controls on Facebook and determine how well existing controls align with these needs. Our survey results highlight a range of user objectives related to controlling Facebook ads, including being able to select what ad topics are shown or what personal information is used in ad targeting. Some objectives are achievable with Facebook's existing controls, but participants seemed to be unaware of them, suggesting issues of discoverability. In our remote usability study, participants noted areas in which the usability of Facebook's advertising controls could be improved, including the location, layout, and explanation of controls. Additionally, we found that users could be categorized into four groups based on their privacy concerns related to Facebook's data collection practices, objectives for controlling their ad experience, and willingness to engage with advertising controls. Our findings provide a set of user requirements for advertising controls, applicable to Facebook as well as other platforms, that would better align such controls with users' needs and expectations.

In this paper we describe the iterative evaluation and refinement of a consent flow for a chatbot being developed by a large U.S. health insurance company. This chatbot’s use of a cloud service provider triggers a requirement for users to agree to a HIPAA authorization. We highlight remote usability study and online survey findings indicating that simplifying the interface and language of the consent flow can improve the user experience and help users who read the content understand how their data may be used. However, we observe that most users in our studies, even those using our improved consent flows, missed important information in the authorization until we asked them to review it again. We also show that many people are overconfident about the privacy and security of healthcare data and that many people believe HIPAA protects in far more contexts than it actually does. Given that our redesigns following best practices did not produce many meaningful improvements in informed consent, we argue for the need for research on alternate approaches to health data disclosures such as standardized disclosures; methods borrowed from clinical research contexts such as multimedia formats, quizzes, and conversational approaches; and automated privacy assistants.

Abstract Browsing privacy tools can help people protect their digital privacy. However, tools which provide the strongest protections—such as Tor Browser—have struggled to achieve widespread adoption. This may be due to usability challenges, misconceptions, behavioral biases, or mere lack of awareness. In this study, we test the effectiveness of nudging interventions that encourage the adoption of Tor Browser. First, we test an informational nudge based on protection motivation theory (PMT), designed to raise awareness of Tor Browser and help participants form accurate perceptions of it. Next, we add an action planning implementation intention, designed to help participants identify opportunities for using Tor Browser. Finally, we add a coping planning implementation intention, designed to help participants overcome challenges to using Tor Browser, such as extreme website slowness. We test these nudges in a longitudinal field experiment with 537 participants. We find that our PMT-based intervention increased use of Tor Browser in both the short- and long-term. Our coping planning nudge also increased use of Tor Browser, but only in the week following our intervention. We did not find statistically significant evidence of our action planning nudge increasing use of Tor Browser. Our study contributes to a greater understanding of factors influencing the adoption of Tor Browser, and how nudges might be used to encourage the adoption of Tor Browser and similar privacy enhancing technologies.

Standardized privacy labels that succinctly summarize those data practices that people are most commonly concerned about offer the promise of providing users with more effective privacy notices than full-length privacy policies. With their introduction by Apple in iOS 14 and Google’s recent adoption in its Play Store, mobile app privacy labels are for the first time available at scale to users. We report the first indepth interview study with 24 lay iPhone users to investigate their experiences, understanding, and perceptions of Apple’s privacy labels. We uncovered misunderstandings of and dissatisfaction with the iOS privacy labels that hinder their effectiveness, including confusing structure, unfamiliar terms, and disconnection from permission settings and controls. We identify areas where app privacy labels might be improved and propose suggestions to address shortcomings to make them more understandable, usable, and useful.

With the ubiquity of web tracking, information on how people navigate the internet is abundantly collected yet, due to its proprietary nature, rarely distributed. As a result, our understanding of user browsing primarily derives from small-scale studies conducted more than a decade ago. To provide an broader updated perspective, we analyze data from 257 participants who consented to have their home computer and browsing behavior monitored through the Security Behavior Observatory. Compared to previous work, we find a substantial increase in tabbed browsing and demonstrate the need to include tab information for accurate web measurements. Our results confirm that user browsing is highly centralized, with 50% of internet use spent on 1% of visited websites. However, we also find that users spend a disproportionate amount of time on low-visited websites, areas with a greater likelihood of containing risky content. We then identify the primary gateways to these sites and discuss implications for future research.

Increasingly, icons are being proposed to concisely convey privacy-related information and choices to users. However, complex privacy concepts can be difficult to communicate. We investigate which icons effectively signal the presence of privacy choices. In a series of user studies, we designed and evaluated icons and accompanying textual descriptions (link texts) conveying choice, opting-out, and sale of personal information — the latter an opt-out mandated by the California Consumer Privacy Act (CCPA). We identified icon-link text pairings that conveyed the presence of privacy choices without creating misconceptions, with a blue stylized toggle icon paired with “Privacy Options” performing best. The two CCPA-mandated link texts (“Do Not Sell My Personal Information” and “Do Not Sell My Info”) accurately communicated the presence of do-not-sell opt-outs with most icons. Our results provide insights for the design of privacy choice indicators and highlight the necessity of incorporating user testing into policy making.

Surveillance of communication between incarcerated and non-incarcerated people has steadily increased, enabled partly by technological advancements. Third-party vendors control communication tools for most U.S. prisons and jails and offer surveillance capabilities beyond what individual facilities could realistically implement. Frequent communication with family improves mental health and post-carceral outcomes for incarcerated people, but does discomfort about surveillance affect how their relatives communicate with them? To explore this and the understanding, attitudes, and reactions to surveillance, we conducted 16 semi-structured interviews with participants who have incarcerated relatives. Among other findings, we learn that participants communicate despite privacy concerns that they felt helpless to address. We also observe inaccuracies in participants’ beliefs about surveillance practices. We discuss implications of inaccurate understandings of surveillance, misaligned incentives between end-users and vendors, how our findings enhance ongoing conversations about carceral justice, and recommendations for more privacy-sensitive communication tools.

In prior work, researchers proposed an Internet of Things (IoT) security and privacy label akin to a food nutrition label, based on input from experts. We conducted a survey with 1,371 Mechanical Turk (MTurk) participants to test the effectiveness of each of the privacy and security attribute-value pairs proposed in that prior work along two key dimensions: ability to convey risk to consumers and impact on their willingness to purchase an IoT device. We found that the values intended to communicate increased risk were generally perceived that way by participants. For example, we found that consumers perceived more risk when a label conveyed that data would be sold to third parties than when it would not be sold at all, and that consumers were more willing to purchase devices when they knew that their data would not be retained or shared with others. However, participants’ risk perception did not always align with their willingness to purchase, sometimes due to usability concerns. Based on our findings, we propose actionable recommendations on how to more effectively present privacy and security attributes on an IoT label to better communicate risk to consumers.

Abstract Privacy and security tools can help users protect themselves online. Unfortunately, people are often unaware of such tools, and have potentially harmful misconceptions about the protections provided by the tools they know about. Effectively encouraging the adoption of privacy tools requires insights into people’s tool awareness and understanding. Towards that end, we conducted a demographically-stratified survey of 500 US participants to measure their use of and perceptions about five web browsing-related tools: private browsing, VPNs, Tor Browser, ad blockers, and antivirus software. We asked about participants’ perceptions of the protections provided by these tools across twelve realistic scenarios. Our thematic analysis of participants’ responses revealed diverse forms of misconceptions. Some types of misconceptions were common across tools and scenarios, while others were associated with particular combinations of tools and scenarios. For example, some participants suggested that the privacy protections offered by private browsing, VPNs, and Tor Browser would also protect them from security threats – a misconception that might expose them to preventable risks. We anticipate that our findings will help researchers, tool designers, and privacy advocates educate the public about privacy- and security-enhancing technologies.

Abstract Cameras are everywhere, and are increasingly coupled with video analytics software that can identify our face, track our mood, recognize what we are doing, and more. We present the results of a 10-day in-situ study designed to understand how people feel about these capabilities, looking both at the extent to which they expect to encounter them as part of their everyday activities and at how comfortable they are with the presence of such technologies across a range of realistic scenarios. Results indicate that while some widespread deployments are expected by many (e.g., surveillance in public spaces), others are not, with some making people feel particularly uncomfortable. Our results further show that individuals’ privacy preferences and expectations are complicated and vary with a number of factors such as the purpose for which footage is captured and analyzed, the particular venue where it is captured, and whom it is shared with. Finally, we discuss the implications of people’s rich and diverse preferences on opt-in or opt-out rights for the collection and use (including sharing) of data associated with these video analytics scenarios as mandated by regulations. Because of the user burden associated with the large number of privacy decisions people could be faced with, we discuss how new types of privacy assistants could possibly be configured to help people manage these decisions.

Usable privacy and security researchers have developed a variety of approaches to represent risk to research participants. To understand how these approaches are used and when each might be most appropriate, we conducted a systematic literature review of methods used in security and privacy studies with human participants. From a sample of 633 papers published at five top conferences between 2014 and 2018 that included keywords related to both security/privacy and usability, we systematically selected and analyzed 284 full-length papers that included human subjects studies. Our analysis focused on study methods; risk representation; the use of prototypes, scenarios, and educational intervention; the use of deception to simulate risk; and types of participants. We discuss benefits and shortcomings of the methods, and identify key methodological, ethical, and research challenges when representing and assessing security and privacy risk. We also provide guidelines for the reporting of user studies in security and privacy.

Multiple mechanisms exist to encourage users to create stronger passwords, including minimum-length and character-class requirements, prohibiting blocklisted passwords, and giving feedback on the strength of candidate passwords. Despite much research, there is little definitive, scientific guidance on how these mechanisms should be combined and configured to best effect. Through two online experiments, we evaluated combinations of minimum-length and character-class requirements, blocklists, and a minimum-strength requirement that requires passwords to exceed a strength threshold according to neural-network-driven password-strength estimates.

Internet of Things (IoT) devices create new ways through which personal data is collected and processed by service providers. Frequently, end users have little awareness of, and even less control over, these devices' data collection. IoT Personalized Privacy Assistants (PPAs) can help overcome this issue by helping users discover and, when available, control the data collection practices of nearby IoT resources. We use semi-structured interviews with 17 participants to explore user perceptions of three increasingly more autonomous potential implementations of PPAs, identifying benefits and issues associated with each implementation. We find that participants weigh the desire for control against the fear of cognitive overload. We recommend solutions that address users' differing automation preferences and reduce notification overload. We discuss open issues related to opting out from public data collections, automated consent, the phenomenon of user resignation, and designing PPAs with at-risk communities in mind.

We conducted an in-lab user study with 24 participants to explore the usefulness and usability of privacy choices offered by websites. Participants were asked to find and use choices related to email marketing, targeted advertising, or data deletion on a set of nine websites that differed in terms of where and how these choices were presented. They struggled with several aspects of the interaction, such as selecting the correct page from a site's navigation menu and understanding what information to include in written opt-out requests. Participants found mechanisms located in account settings pages easier to use than options contained in privacy policies, but many still consulted help pages or sent email to request assistance. Our findings indicate that, despite their prevalence, privacy choices like those examined in this study are difficult for consumers to exercise in practice. We provide design and policy recommendations for making these website opt-out and deletion choices more useful and usable for consumers.

Information about the privacy and security of Internet of Things (IoT) devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers-a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts' rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT device (smart home device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoT-related purchase decisions.

Website privacy policies sometimes provide users the option to opt-out of certain collections and uses of their personal data. Unfortunately, many privacy policies bury these instructions deep in their text, and few web users have the time or skill necessary to discover them. We describe a method for the automated detection of opt-out choices in privacy policy text and their presentation to users through a web browser extension. We describe the creation of two corpora of opt-out choices, which enable the training of classifiers to identify opt-outs in privacy policies. Our overall approach for extracting and classifying opt-out choices combines heuristics to identify commonly found opt-out hyperlinks with supervised machine learning to automatically identify less conspicuous instances. Our approach achieves a precision of 0.93 and a recall of 0.9. We introduce Opt-Out Easy, a web browser extension designed to present available opt-out choices to users as they browse the web. We evaluate the usability of our browser extension with a user study. We also present results of a large-scale analysis of opt-outs found in the text of thousands of the most popular websites.

Despite growing concerns about security and privacy of Internet of Things (IoT) devices, consumers generally do not have access to security and privacy information when purchasing these devices. We interviewed 24 participants about IoT devices they purchased. While most had not considered privacy and security prior to purchase, they reported becoming concerned later due to media reports, opinions shared by friends, or observing unexpected device behavior. Those who sought privacy and security information before purchase, reported that it was difficult or impossible to find. We asked interviewees to rank factors they would consider when purchasing IoT devices; after features and price, privacy and security were ranked among the most important. Finally, we showed interviewees our prototype privacy and security label. Almost all found it to be accessible and useful, encouraging them to incorporate privacy and security in their IoT purchase decisions.

OBJECTIVE: We sought to present a model of privacy disposition and its development based on qualitative research on privacy considerations in the context of emerging health technologies. MATERIALS AND METHODS: We spoke to 108 participants across 44 interviews and 9 focus groups to understand the range of ways in which individuals value (or do not value) control over their health information. Transcripts of interviews and focus groups were systematically coded and analyzed in ATLAS.ti for privacy considerations expressed by respondents. RESULTS: Three key findings from the qualitative data suggest a model of privacy disposition. First, participants described privacy related behavior as both contextual and habitual. Second, there are motivations for and deterrents to sharing personal information that do not fit into the analytical categories of risks and benefits. Third, philosophies of privacy, often described as attitudes toward privacy, should be classified as a subtype of motivation or deterrent. DISCUSSION: This qualitative analysis suggests a simple but potentially powerful conceptual model of privacy disposition, or what makes a person more or less private. Components of privacy disposition are identifiable and measurable through self-report and therefore amenable to operationalization and further quantitative inquiry. CONCLUSIONS: We propose this model as the basis for a psychometric instrument that can be used to identify types of privacy dispositions, with potential applications in research, clinical practice, system design, and policy.

Despite the additional protection it affords, two-factor authentication (2FA) adoption reportedly remains low. To better understand 2FA adoption and its barriers, we observed the deployment of a 2FA system at Carnegie Mellon University (CMU). We explore user behaviors and opinions around adoption, surrounding a mandatory adoption deadline. Our results show that (a) 2FA adopters found it annoying, but fairly easy to use, and believed it made their accounts more secure; (b) experience with CMU Duo often led to positive perceptions, sometimes translating into 2FA adoption for other accounts; and, (c) the differences between users required to adopt 2FA and those who adopted voluntarily are smaller than expected. We also explore the relationship between different usage patterns and perceived usability, and identify user misconceptions, insecure practices, and design issues. We conclude with recommendations for large-scale 2FA deployments to maximize adoption, focusing on implementation design, use of adoption mandates, and strategic messaging.

As increasingly many Internet-of-Things (IoT) devices collect personal data, users face more privacy decisions. Personal privacy assistants can provide social cues and help users make informed decisions by presenting information about how others have decided in similar cases. To better understand which social cues are relevant and whose recommendations users are more likely to follow, we presented 1000 online participants with nine IoT data-collection scenarios. Some participants were told the percentage of experts or friends who allowed data collection in each scenario, while other participants were provided no social cue. At the conclusion of each scenario, participants were asked whether they would allow the described data collection. Our results help explain under what circumstances users are more or less likely to be swayed by the reported behavior of others in similar scenarios. For example, our results indicate that when friends denied data collection, our participants were more influenced than when friends allowed data collection. On the other hand, participants were more influenced by experts when they allowed data collection. We also observed that influence could get stronger or wear off when participants were exposed to a sequence of scenarios. For example, when experts and friends repeatedly allowed data collection in scenarios with clear risk or denied it in scenarios with clear benefits, participants were less likely to be influenced by them in subsequent scenarios.

Abstract Are the many formal definitions and frameworks of privacy consistent with a layperson’s understanding of privacy? We explored this question and identified mental models and metaphors of privacy, conceptual tools that can be used to improve privacy tools, communication, and design for everyday users. Our investigation focused on a qualitative analysis of 366 drawings of privacy from laypeople, privacy experts, children, and adults. Illustrators all responded to the prompt “What does privacy mean to you?” We coded each image for content, identifying themes from established privacy frameworks and defining the visual and conceptual metaphors illustrators used to model privacy. We found that many non-expert drawings illustrated a strong divide between public and private physical spaces, while experts were more likely to draw nuanced data privacy spaces. Young children’s drawings focused on bedrooms, bathrooms, or cheating on schoolwork, and seldom addressed data privacy. The metaphors, themes, and symbols identified by these findings can be used for improving privacy communication, education, and design by inspiring and informing visual and conceptual strategies for reaching laypeople.

Text passwords---a frequent vector for account compromise, yet still ubiquitous---have been studied for decades by researchers attempting to determine how to coerce users to create passwords that are hard for attackers to guess but still easy for users to type and memorize. Most studies examine one password or a small number of passwords per user, and studies often rely on passwords created solely for the purpose of the study or on passwords protecting low-value accounts. These limitations severely constrain our understanding of password security in practice, including the extent and nature of password reuse, password behaviors specific to categories of accounts (e.g., financial websites), and the effect of password managers and other privacy tools.

General-purpose content-sharing platforms make it difficult for users to limit sharing to people interested in particular topics. Additional topic-based controls may allow users to better reach desired audiences. Designing such tools requires understanding current interest-based targeting techniques and the potential impact of additional mechanisms. We present an exploratory, interview-based study (n = 16) that addresses these dynamics for Facebook. We use diary-driven probes to explore general topic-based sharing across applications. We then use Facebook-based mockups to probe use cases and design tensions around adding topic-based sharing mechanisms to Facebook. We find that participants currently draw on various audience-limiting and reaching strategies to target interest-based audiences. Participants felt additional topic-based sharing mechanisms on Facebook might allow them to avoid oversharing or offending others and allow them to target improved audiences or share improved content. Usable topic-based sharing tools would also need to account, however, for participants' varied desired engagement strategies.

Many authentication schemes ask users to manually compare compact representations of cryptographic keys, known as fingerprints. If the fingerprints do not match, that may signal a man-in-the-middle attack. An adversary performing an attack may use a fingerprint that is similar to the target fingerprint, but not an exact match, to try to fool inattentive users. Fingerprint representations should thus be both usable and secure. We tested the usability and security of eight fingerprint representations under different configurations. In a 661-participant between-subjects experiment, participants compared fingerprints under realistic conditions and were subjected to a simulated attack. The best configuration allowed attacks to succeed 6% of the time; the worst 72%. We find the seemingly effective compare-and-select approach performs poorly for key fingerprints and that graphical fingerprint representations, while intuitive and fast, vary in performance. We identify some fingerprint representations as particularly promising.

Despite their ubiquity, many password meters provide inaccurate strength estimates. Furthermore, they do not explain to users what is wrong with their password or how to improve it. We describe the development and evaluation of a data-driven password meter that provides accurate strength measurement and actionable, detailed feedback to users. This meter combines neural networks and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about the user's password. We describe the meter's iterative development and final design. We detail the security and usability impact of the meter's design dimensions, examined through a 4,509-participant online study. Under the more common password-composition policy we tested, we found that the data-driven meter with detailed feedback led users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator.

Recent research has improved our understanding of how to create strong, memorable text passwords. However, this research has generally been in the context of desktops and laptops, while users are increasingly creating and entering passwords on mobile devices. In this paper we study whether recent password guidance carries over to the mobile setting. We compare the strength and usability of passwords created and used on mobile devices with those created and used on desktops and laptops, while varying password policy requirements and input methods. We find that creating passwords on mobile devices takes significantly longer and is more error prone and frustrating. Passwords created on mobile devices are also weaker, but only against attackers who can make more than 10^13 guesses. We find that the effects of password policies differ between the desktop and mobile environments, and suggest ways to ease password entry for mobile users.

People share personal content online with varied audiences, as part of tasks ranging from conversational-style content sharing to collaborative activities. We use an interview- and diary-based study to explore: 1) what factors impact channel choice for sharing with particular audiences; and 2) what behavioral patterns emerge from the ability to combine or switch between channels. We find that in the context of different tasks, participants match channel features to selective-sharing and other task-based needs, shaped by recipient attributes and communication dynamics. Participants also combine multiple channels to create composite sharing features or reach broader audiences when one channel is insufficient. We discuss design implications of these channel dynamics.

Although many users create predictable passwords, the extent to which users realize these passwords are predictable is not well understood. We investigate the relationship between users' perceptions of the strength of specific passwords and their actual strength. In this 165-participant online study, we ask participants to rate the comparative security of carefully juxtaposed pairs of passwords, as well as the security and memorability of both existing passwords and common password-creation strategies. Participants had serious misconceptions about the impact of basing passwords on common phrases and including digits and keyboard patterns in passwords. However, in most other cases, participants' perceptions of what characteristics make a password secure were consistent with the performance of current password-cracking tools. We find large variance in participants' understanding of how passwords may be attacked, potentially explaining why users nonetheless make predictable passwords. We conclude with design directions for helping users make better passwords.

Password-composition policies are the result of service providers becoming increasingly concerned about the security of online accounts. These policies restrict the space of user-created passwords to preclude easily guessed passwords and thus make passwords more difficult for attackers to guess. However, many users struggle to create and recall their passwords under strict password-composition policies, for example, ones that require passwords to have at least eight characters with multiple character classes and a dictionary check. Recent research showed that a promising alternative was to focus policy requirements on password length instead of on complexity. In this work, we examine 15 password policies, many focusing on length requirements. In doing so, we contribute the first thorough examination of policies requiring longer passwords. We conducted two online studies with over 20,000 participants, and collected both usability and password-strength data. Our findings indicate that password strength and password usability are not necessarily inversely correlated: policies that lead to stronger passwords do not always reduce usability. We identify policies that are both more usable and more secure than commonly used policies that emphasize complexity rather than length requirements. We also provide practical recommendations for service providers who want their users to have strong yet usable passwords.

Smartphone users are often unaware of the data collected by apps running on their devices. We report on a study that evaluates the benefits of giving users an app permission manager and sending them nudges intended to raise their awareness of the data collected by their apps. Our study provides both qualitative and quantitative evidence that these approaches are complementary and can each play a significant role in empowering users to more effectively control their privacy. For instance, even after a week with access to the permission manager, participants benefited from nudges showing them how often some of their sensitive data was being accessed by apps, with 95% of participants reassessing their permissions, and 58% of them further restricting some of their permissions. We discuss how participants interacted both with the permission manager and the privacy nudges, analyze the effectiveness of both solutions, and derive some recommendations.

Users often struggle to create passwords under strict requirements. To make this process easier, some providers present real-time feedback during password creation, indicating which requirements are not yet met. Other providers guide users through a multi-step password-creation process. Our 6,435-participant online study examines how feedback and guidance affect password security and usability. We find that real-time password-creation feedback can help users create strong passwords with fewer errors. We also find that although guiding participants through a three-step password-creation process can make creation easier, it may result in weaker passwords. Our results suggest that service providers should present password requirements with feedback to increase usability. However, the presentation of feedback and guidance must be carefully considered, since identical requirements can have different security and usability effects depending on presentation.

Despite benefits and uses of social networking sites (SNSs) users are not always satisfied with their behaviors on the sites. These desires for behavior change both provide insight into users' perceptions of how SNSs impact their lives (positively or negatively) and can inform tools for helping users achieve desired behavior changes. We use a 604-participant online survey to explore SNS users' behavior-change goals for Facebook, Instagram, and Twitter. While some participants want to reduce site use, others want to improve their use or increase a range of behaviors. These desired changes differ by SNS, and, for Twitter, by participants' levels of site use. Participants also expect a range of benefits from these goals, including increased time, contact with others, intrinsic benefits, better security/privacy, and improved self presentation. Based on these results we provide insights both into how participants perceive different SNSs, as well as potential designs for behavior-change mechanisms to target SNS behaviors.

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.

To encourage strong passwords, system administrators employ password-composition policies, such as a traditional policy requiring that passwords have at least 8 characters from 4 character classes and pass a dictionary check. Recent research has suggested, however, that policies requiring longer passwords with fewer additional requirements can be more usable and in some cases more secure than this traditional policy. To explore long passwords in more detail, we conducted an online experiment with 8,143 participants. Using a cracking algorithm modified for longer passwords, we evaluate eight policies across a variety of metrics for strength and usability. Among the longer policies, we discover new evidence for a security/usability tradeoff, with none being strictly better than another on both dimensions. However, several policies are both more usable and more secure that the traditional policy we tested. Our analyses additionally reveal common patterns and strings found in cracked passwords. We discuss how system administrators can use these results to improve password-composition policies.

Despite considerable research on passwords, empirical studies of password strength have been limited by lack of access to plaintext passwords, small data sets, and password sets specifically collected for a research study or from low-value accounts. Properties of passwords used for high-value accounts thus remain poorly understood.

Smartphones have unprecedented access to sensitive personal information. While users report having privacy concerns, they may not actively consider privacy while downloading apps from smartphone application marketplaces. Currently, Android users have only the Android permissions display, which appears after they have selected an app to download, to help them understand how applications access their information. We investigate how permissions and privacy could play a more active role in app-selection decisions. We designed a short "Privacy Facts' display, which we tested in a 20-participant lab study and a 366-participant online experiment. We found that by bringing privacy information to the user when they were making the decision and by presenting it in a clearer fashion, we could assist users in choosing applications that request fewer permissions.

We present the results of an online survey of 1,221 Twitter users, comparing messages individuals regretted either saying during in-person conversations or posting on Twitter. Participants generally reported similar types of regrets in person and on Twitter. In particular, they often regretted messages that were critical of others. However, regretted messages that were cathartic/expressive or revealed too much information were reported at a higher rate for Twitter. Regretted messages on Twitter also reached broader audiences. In addition, we found that participants who posted on Twitter became aware of, and tried to repair, regret more slowly than those reporting in-person regrets. From this comparison of Twitter and in-person regrets, we provide preliminary ideas for tools to help Twitter users avoid and cope with regret.

Social networking site users must decide what content to share and with whom. Many social networks, including Facebook, provide tools that allow users to selectively share content or block people from viewing content. However, sometimes instead of targeting a particular audience, users will self-censor, or choose not to share. We report the results from an 18-participant user study designed to explore self-censorship behavior as well as the subset of unshared content participants would have potentially shared if they could have specifically targeted desired audiences. We asked participants to report all content they thought about sharing but decided not to share on Facebook and interviewed participants about why they made sharing decisions and with whom they would have liked to have shared or not shared. Participants reported that they would have shared approximately half the unshared content if they had been able to exactly target their desired audiences.

Location sharing is a popular feature of online social networks, but challenges remain in the effective presentation of privacy choices to users, whose location sharing preferences are complex and diverse. One proposed approach for capturing these nuances builds on the observation that key attributes of users' location sharing preferences can be represented by a small number of privacy profiles, which can provide a basis for configuring individual preferences. However, the impact of this approach on how users view their privacy is relatively unknown. We present a study evaluating the impact of this approach on users' location sharing preferences and their satisfaction with the decisions made by their resulting settings. The results suggest that this approach can influence users to share significantly more without a substantial difference in comfort. This further suggests that the provision of profiles for privacy settings must be carefully considered, as they can substantially alter sharing behavior.

Today's smartphone applications expect users to make decisions about what information they are willing to share, but fail to provide sufficient feedback about which privacy-sensitive information is leaving the phone, as well as how frequently and with which entities it is being shared. Such feedback can improve users' understanding of potential privacy leakages through apps that collect information about them in an unexpected way. Through a qualitative lab study with 19 participants, we first discuss misconceptions that smartphone users currently have with respect to two popular game applications that frequently collect the phone's current location and share it with multiple third parties. To measure the gap between users' understanding and actual privacy leakages, we use two types of interfaces that we developed: just-in-time notifications that appear the moment data is shared and a visualization that summarizes the shared data. We then report on participants' perceived benefits and concerns regarding data sharing with smartphone applications after experiencing notifications and having viewed the visualization. We conclude with a discussion on how heightened awareness of users and usable controls can mitigate some of these concerns.

We designed and tested attractors for computer security dialogs: user-interface modifications used to draw users' attention to the most important information for making decisions. Some of these modifications were purely visual, while others temporarily inhibited potentially-dangerous behaviors to redirect users' attention to salient information. We conducted three between-subjects experiments to test the effectiveness of the attractors.

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.

When asking users to enter credentials, today's desktop operating systems often use windows that provide scant evidence that a trusted path has been established; evidence that would allow a user to know that a request is genuine and that the password will not be read by untrusted principals. We measure the efficacy of web-based attacks that spoof these operating system credential-entry windows to steal users' device-login passwords. We recruited 504 users of Amazon's Mechanical Turk to evaluate a series of games on third-party websites. The third such website indicated that it needed to install software from the publisher that provided the participants' operating system: Microsoft's Silverlight for Windows Vista/7 users and Apple's QuickTime for Mac OS users. The website then displayed a spoofed replica of a window the participant's client operating system would use to request a user's device credentials. In our most effective attacks, over 20% of participants entered passwords that they later admitted were the genuine credentials used to login to their devices. Even among those who declined to enter their credentials, many participants were oblivious to the spoofing attack. Participants were more likely to confirm that they were worried about the consequences of installing software from a legitimate source than to report that they thought the credential-entry window might have appeared as a result of an attempt to steal their password.

Users often have rich and complex photo-sharing preferences, but properly configuring access control can be difficult and time-consuming. In an 18-participant laboratory study, we explore whether the keywords and captions with which users tag their photos can be used to help users more intuitively create and maintain access-control policies. We find that (a) tags created for organizational purposes can be repurposed to create efficient and reasonably accurate access-control rules; (b) users tagging with access control in mind develop coherent strategies that lead to significantly more accurate rules than those associated with organizational tags alone; and (c) participants can understand and actively engage with the concept of tag-based access control.

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 take a detailed look at how users, while focusing on non-permission tasks, notice and fix access-control permission errors depending on where the access-control policy is spatially located on a photo-sharing website. The access-control policy was placed on an online photo-sharing website under the photo or album, on the sidebar, or on a separate settings page. We find that placing the access-control policy directly under photos and album thumbnails improves participants' ability to notice errors in their access-control settings without negatively impacting non-access-control tasks.

Users tend to create passwords that are easy to guess, while system-assigned passwords tend to be hard to remember. Passphrases, space-delimited sets of natural language words, have been suggested as both secure and usable for decades. In a 1,476-participant online study, we explored the usability of 3- and 4-word system-assigned passphrases in comparison to system-assigned passwords composed of 5 to 6 random characters, and 8-character system-assigned pronounceable passwords. Contrary to expectations, system-assigned passphrases performed similarly to system-assigned passwords of similar entropy across the usability metrics we examined. Passphrases and passwords were forgotten at similar rates, led to similar levels of user difficulty and annoyance, and were both written down by a majority of participants. However, passphrases took significantly longer for participants to enter, and appear to require error-correction to counteract entry mistakes. Passphrase usability did not seem to increase when we shrunk the dictionary from which words were chosen, reduced the number of words in a passphrase, or allowed users to change the order of words.

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.

Text-based passwords remain the dominant authentication method in computer systems, despite significant advancement in attackers' capabilities to perform password cracking. In response to this threat, password composition policies have grown increasingly complex. However, there is insufficient research defining metrics to characterize password strength and using them to evaluate password-composition policies. In this paper, we analyze 12,000 passwords collected under seven composition policies via an online study. We develop an efficient distributed method for calculating how effectively several heuristic password-guessing algorithms guess passwords. Leveraging this method, we investigate (a) the resistance of passwords created under different conditions to guessing, (b) the performance of guessing algorithms under different training sets, (c) the relationship between passwords explicitly created under a given composition policy and other passwords that happen to meet the same requirements, and (d) the relationship between guess ability, as measured with password-cracking algorithms, and entropy estimates. Our findings advance understanding of both password-composition policies and metrics for quantifying password security.

As smartphones and other mobile computing devices have increased in ubiquity, advertisers have begun to realize a more effective way of targeting users and a promising area for revenue growth: location-based advertising. This trend brings to bear new questions about whether or not users will adopt products involving this potentially invasive form of advertising and what sorts of protections they should be given. Our real-world user study of 27 participants echoes earlier findings that users have significant privacy concerns regarding sharing their locations with advertisers. However, we examine these concerns in more detail and find that they are complex (e.g., relating not only to the quantity of ads, but the locations and times at which they are received). With advanced privacy settings, users stated they would feel more comfortable and share more information than with a simple opt-in/opt-out mechanism.

Text-based passwords are the most common mechanism for authenticating humans to computer systems. To prevent users from picking passwords that are too easy for an adversary to guess, system administrators adopt password-composition policies (e.g., requiring passwords to contain symbols and numbers). Unfortunately, little is known about the relationship between password-composition policies and the strength of the resulting passwords, or about the behavior of users (e.g., writing down passwords) in response to different policies. We present a large-scale study that investigates password strength, user behavior, and user sentiment across four password-composition policies. We characterize the predictability of passwords by calculating their entropy, and find that a number of commonly held beliefs about password composition and strength are inaccurate. We correlate our results with user behavior and sentiment to produce several recommendations for password-composition policies that result in strong passwords without unduly burdening users.

As users store and share more digital content at home, access control becomes increasingly important. One promising approach for helping non-expert users create accurate access policies is reactive policy creation, in which users can update their policy dynamically in response to access requests that would not otherwise succeed. An earlier study suggested reactive policy creation might be a good fit for file access control at home. To test this, we conducted an experience-sampling study in which participants used a simulated reactive access-control system for a week. Our results bolster the case for reactive policy creation as one mode by which home users specify access-control policy. We found both quantitative and qualitative evidence of dynamic, situational policies that are hard to implement using traditional models but that reactive policy creation can facilitate. While we found some clear disadvantages to the reactive model, they do not seem insurmountable.

In access-control systems, policy rules conflict when they prescribe different decisions (allow or deny) for the same access. We present the results of a user study that demonstrates the significant impact of conflict-resolution method on policy-authoring usability. In our study of 54 participants, varying the conflict-resolution method yielded statistically significant differences in accuracy in five of the six tasks we tested, including differences in accuracy rates of up to 78%. Our results suggest that a conflict-resolution method favoring rules of smaller scope over rules of larger scope is more usable than the Microsoft Windows operating system's method of favoring deny rules over allow rules. Perhaps more importantly, our results demonstrate that even seemingly small changes to a system's semantics can fundamentally affect the system's usability in ways that are beyond the power of user interfaces to correct.

As ubiquitous computing becomes increasingly mobile and social, personal information sharing will likely increase in frequency, the variety of friends to share with, and range of information that can be shared. Past work has identified that whom you share with is important for choosing whether or not to share, but little work has explored which features of interpersonal relationships influence sharing. We present the results of a study of 42 participants, who self-report aspects of their relationships with 70 of their friends, including frequency of collocation and communication, closeness, and social group. Participants rated their willingness to share in 21 different scenarios based on information a UbiComp system could provide. Our findings show that (a) self-reported closeness is the strongest indicator of willingness to share, (b) individuals are more likely to share in scenarios with common information (e.g. we are within one mile of each other) than other kinds of scenarios (e.g. my location wherever I am), and (c) frequency of communication predicts both closeness and willingness to share better than frequency of collocation.

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.

Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we propose a layered anti-phishing solution that aims at (1) exploiting the expressiveness of a rich set of features with machine learning to achieve a high true positive rate (TP) on novel phish, and (2) limiting the FP to a low level via filtering algorithms. Specifically, we proposed CANTINA+, the most comprehensive feature-based approach in the literature including eight novel features, which exploits the HTML Document Object Model (DOM), search engines and third party services with machine learning techniques to detect phish. Moreover, we designed two filters to help reduce FP and achieve runtime speedup. The first is a near-duplicate phish detector that uses hashing to catch highly similar phish. The second is a login form filter, which directly classifies Web pages with no identified login form as legitimate. We extensively evaluated CANTINA+ with two methods on a diverse spectrum of corpora with 8118 phish and 4883 legitimate Web pages. In the randomized evaluation, CANTINA+ achieved over 92% TP on unique testing phish and over 99% TP on near-duplicate testing phish, and about 0.4% FP with 10% training phish. In the time-based evaluation, CANTINA+ also achieved over 92% TP on unique testing phish, over 99% TP on near-duplicate testing phish, and about 1.4% FP under 20% training phish with a two-week sliding window. Capable of achieving 0.4% FP and over 92% TP, our CANTINA+ has been demonstrated to be a competitive anti-phishing solution.

In this paper we discuss a screening process used in conjunction with a survey administered via Amazon.com's Mechanical Turk. We sought an easily implementable method to disqualify those people who participate but don't take the study tasks seriously. By using two previously pilot tested screening questions, we identified 764 of 1,962 people who did not answer conscientiously. Young men seem to be most likely to fail the qualification task. Those that are professionals, students, and non-workers seem to be more likely to take the task seriously than financial workers, hourly workers, and other workers. Men over 30 and women were more likely to answer seriously.

Earlier work has shown that consumers cannot effectively find information in privacy policies and that they do not enjoy using them. In our previous research we developed a standardized table format for privacy policies. We compared this standardized format, and two short variants (one tabular, one text) with the current status quo: full text natural-language policies and layered policies. We conducted an online user study of 764 participants to test if these three more-intentionally designed, standardized privacy policy formats, assisted by consumer education, can benefit consumers. Our results show that standardized privacy policy presentations can have significant positive effects on accuracy and speed of information finding and on reader enjoyment of privacy policies.

As digital content becomes more prevalent in the home, non-technical users are increasingly interested in sharing that content with others and accessing it from multiple devices. Not much is known about how these users think about controlling access to this data. To better understand this, we conducted semi-structured, in-situ interviews with 33 users in 15 households. We found that users create ad-hoc access-control mechanisms that do not always work; that their ideal policies are complex and multi-dimensional; that a priori policy specification is often insufficient; and that people's mental models of access control and security are often misaligned with current systems. We detail these findings and present a set of associated guidelines for designing usable access-control systems for the home environment.

In this paper we present the results of a roleplay survey instrument administered to 1001 online survey respondents to study both the relationship between demographics and phishing susceptibility and the effectiveness of several anti-phishing educational materials. Our results suggest that women are more susceptible than men to phishing and participants between the ages of 18 and 25 are more susceptible to phishing than other age groups. We explain these demographic factors through a mediation analysis. Educational materials reduced users' tendency to enter information into phishing webpages by 40% percent; however, some of the educational materials we tested also slightly decreased participants' tendency to click on legitimate links.

The rapid adoption of location tracking and mobile social networking technologies raises significant privacy challenges. Today our understanding of people's location sharing privacy preferences remains very limited, including how these preferences are impacted by the type of location tracking device or the nature of the locations visited. To address this gap, we deployed Locaccino, a mobile location sharing system, in a four week long field study, where we examined the behavior of study participants (n=28) who shared their location with their acquaintances (n=373.) Our results show that users appear more comfortable sharing their presence at locations visited by a large and diverse set of people. Our study also indicates that people who visit a wider number of places tend to also be the subject of a greater number of requests for their locations. Over time these same people tend to also evolve more sophisticated privacy preferences, reflected by an increase in time- and location-based restrictions. We conclude by discussing the implications our findings.

Text-based passwords are still the most commonly used authentication mechanism in information systems. We took advantage of a unique opportunity presented by a significant change in the Carnegie Mellon University (CMU) computing services password policy that required users to change their passwords. Through our survey of 470 CMU computer users, we collected data about behaviors and practices related to the use and creation of passwords. We also captured users' opinions about the new, stronger policy requirements. Our analysis shows that, although most of the users were annoyed by the need to create a complex password, they believe that they are now more secure. Furthermore, we perform an entropy analysis and discuss how our findings relate to NIST recommendations for creating a password policy. We also examine how users answer specific questions related to their passwords. Our results can be helpful in designing better password policies that consider not only technical aspects of specific policy rules, but also users' behavior in response to those rules.

Phishing attacks, in which criminals lure Internet users to Web sites that spoof legitimate Web sites, are occurring with increasing frequency and are causing considerable harm to victims. While a great deal of effort has been devoted to solving the phishing problem by prevention and detection of phishing emails and phishing Web sites, little research has been done in the area of training users to recognize those attacks. Our research focuses on educating users about phishing and helping them make better trust decisions. We identified a number of challenges for end-user security education in general and anti-phishing education in particular: users are not motivated to learn about security; for most users, security is a secondary task; it is difficult to teach people to identify security threats without also increasing their tendency to misjudge nonthreats as threats. Keeping these challenges in mind, we developed an email-based anti-phishing education system called “PhishGuru” and an online game called “Anti-Phishing Phil” that teaches users how to use cues in URLs to avoid falling for phishing attacks. We applied learning science instructional principles in the design of PhishGuru and Anti-Phishing Phil. In this article we present the results of PhishGuru and Anti-Phishing Phil user studies that demonstrate the effectiveness of these tools. Our results suggest that, while automated detection systems should be used as the first line of defense against phishing attacks, user education offers a complementary approach to help people better recognize fraudulent emails and websites.

In this work we ask the question: what are the challenges of managing a physical or file system access-control policy for a large organization? To answer the question, we conducted a series of interviews with thirteen administrators who manage access-control policy for either a file system or a physical space. Based on these interviews we identified three sets of real-world requirements that are either ignored or inadequately addressed by technology: 1) policies are made/implemented by multiple people; 2) policy makers are distinct from policy implementers; and 3) access-control systems don't always have the capability to implement the desired policy. We present our interview results and propose several possible solutions to address the observed issues.

Many commerce websites post privacy policies to address Internet shoppers' privacy concerns. However, few users read or understand them. Iconic privacy indicators may make privacy policies more accessible and easier for users to understand: in this paper, we examine whether the timing and placement of online privacy indicators impact Internet users' browsing and purchasing decisions. We conducted a laboratory study where we controlled the placement of privacy information, the timing of its appearance, the privacy level of each website, and the price and items being purchased. We found that the timing of privacy information had a significant impact on how much of a premium users were willing to pay for privacy. We also found that timing had less impact when users were willing to examine multiple websites. Finally, we found that users paid more attention to privacy indicators when purchasing privacy-sensitive items than when purchasing items that raised minimal privacy concerns.

Feedback is viewed as an essential element of ubiquitous computing systems in the HCI literature for helping people manage their privacy. However, the success of online social networks and existing commercial systems for mobile location sharing which do not incorporate feedback would seem to call the importance of feedback into question. We investigated this issue in the context of a mobile location sharing system. Specifically, we report on the findings of a field de-ployment of Locyoution, a mobile location sharing system. In our study of 56 users, one group was given feedback in the form of a history of location requests, and a second group was given no feedback at all. Our major contribution has been to show that feedback is an important contributing factor towards improving user comfort levels and allaying privacy concerns. Participants' privacy concerns were reduced after using the mobile location sharing system. Additionally,our study suggests that peer opinion and technical savviness contribute most to whether or not participants thought they would continue to use a mobile location technology.

No abstract available.

We used an iterative design process to develop a privacy label that presents to consumers the ways organizations collect, use, and share personal information. Many surveys have shown that consumers are concerned about online privacy, yet current mechanisms to present website privacy policies have not been successful. This research addresses the present gap in the communication and understanding of privacy policies, by creating an information design that improves the visual presentation and comprehensibility of privacy policies. Drawing from nutrition, warning, and energy labeling, as well as from the effort towards creating a standardized banking privacy notification, we present our process for constructing and refining a label tuned to privacy. This paper describes our design methodology; findings from two focus groups; and accuracy, timing, and likeability results from a laboratory study with 24 participants. Our study results demonstrate that compared to existing natural language privacy policies, the proposed privacy label allows participants to find information more quickly and accurately, and provides a more enjoyable information seeking experience.

PhishGuru is an embedded training system that teaches users to avoid falling for phishing attacks by delivering a training message when the user clicks on the URL in a simulated phishing email. In previous lab and real-world experiments, we validated the effectiveness of this approach. Here, we extend our previous work with a 515-participant, real-world study in which we focus on long-term retention and the effect of two training messages. We also investigate demographic factors that influence training and general phishing susceptibility. Results of this study show that (1) users trained with PhishGuru retain knowledge even after 28 days; (2) adding a second training message to reinforce the original training decreases the likelihood of people giving information to phishing websites; and (3) training does not decrease users' willingness to click on links in legitimate messages. We found no significant difference between males and females in the tendency to fall for phishing emails both before and after the training. We found that participants in the 18--25 age group were consistently more vulnerable to phishing attacks on all days of the study than older participants. Finally, our exit survey results indicate that most participants enjoyed receiving training during their normal use of email.

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Feedback is viewed as an essential element of ubiquitous computing systems in the HCI literature for helping people manage their privacy. However, the success of online social networks and existing commercial systems for mobile location sharing which do not incorporate feedback would seem to call the importance of feedback into question. We investigated this issue in the context of a mobile location sharing system. Specifically, we report on the findings of a field de-ployment of Locyoution, a mobile location sharing system. In our study of 56 users, one group was given feedback in the form of a history of location requests, and a second group was given no feedback at all. Our major contribution has been to show that feedback is an important contributing factor towards improving user comfort levels and allaying privacy concerns. Participants' privacy concerns were reduced after using the mobile location sharing system. Additionally,our study suggests that peer opinion and technical savviness contribute most to whether or not participants thought they would continue to use a mobile location technology.

In this paper we integrate insights from diverse islands of research on electronic privacy to offer a holistic view of privacy engineering and a systematic structure for the discipline's topics. First we discuss privacy requirements grounded in both historic and contemporary perspectives on privacy. We use a three-layer model of user privacy concerns to relate them to system operations (data transfer, storage and processing) and examine their effects on user behavior. In the second part of the paper we develop guidelines for building privacy-friendly systems. We distinguish two approaches: "privacy-by-policy" and "privacy-by-architecture." The privacy-by-policy approach focuses on the implementation of the notice and choice principles of fair information practices (FIPs), while the privacy-by-architecture approach minimizes the collection of identifiable personal data and emphasizes anonymization and client-side data storage and processing. We discuss both approaches with a view to their technical overlaps and boundaries as well as to economic feasibility. The paper aims to introduce engineers and computer scientists to the privacy research domain and provide concrete guidance on how to design privacy-friendly systems.

A large number of papers have proposed cryptographic protocols for establishing secure group communication. These protocols allow a set of group members to exchange or establish keys to encrypt and authenticate messages within the group. At the same time, individuals outside of the group cannot eavesdrop on group communication or inject messages. There have even been usability studies, demonstrating an average user can successfully complete some of these protocols. However, group protocols are rarely used in the real world. In this work, we conduct a survey to help uncover why the general population ignores such mechanisms for group communication. We also try to determine what protocols would best match respondents' current expectations for group protocols and methods for establishing trust. Survey results indicate that a group protocol that leverages location-limited channels, PKI, or Web-of-Trust authenticated public keys and allows addition and deletion of members fulfills the majority of users' expectations.

Significant effort has been invested in developing expressive and flexible access-control languages and systems. However, little has been done to evaluate these systems in practical situations with real users, and few attempts have been made to discover and analyze the access-control policies that users actually want to implement. We report on a user study in which we derive the ideal access policies desired by a group of users for physical security in an office environment. We compare these ideal policies to the policies the users actually implemented with keys and with a smartphone-based distributed access-control system. We develop a methodology that allows us to show quantitatively that the smartphone system allowed our users to implement their ideal policies more accurately and securely than they could with keys, and we describe where each system fell short.

Many popular web browsers are now including active phishing warnings after previous research has shown that passive warnings are often ignored. In this laboratory study we examine the effectiveness of these warnings and examine if, how, and why they fail users. We simulated a spear phishing attack to expose users to browser warnings. We found that 97% of our sixty participants fell for at least one of the phishing messages that we sent them. However, we also found that when presented with the active warnings, 79% of participants heeded them, which was not the case for the passive warning that we tested---where only one participant heeded the warnings. Using a model from the warning sciences we analyzed how users perceive warning messages and offer suggestions for creating more effective warning messages within the phishing context.

We introduce the Expandable Grid, a novel interaction technique for creating, editing, and viewing many types of security policies. Security policies, such as file permissions policies, have traditionally been displayed and edited in user interfaces based on a list of rules, each of which can only be viewed or edited in isolation. These list-of-rules interfaces cause problems for users when multiple rules interact, because the interfaces have no means of conveying the interactions amongst rules to users. Instead, users are left to figure out these rule interactions themselves. An Expandable Grid is an interactive matrix visualization designed to address the problems that list-of-rules interfaces have in conveying policies to users. This paper describes the Expandable Grid concept, shows a system using an Expandable Grid for setting file permissions in the Microsoft Windows XP operating system, and gives results of a user study involving 36 participants in which the Expandable Grid approach vastly outperformed the native Windows XP file-permissions interface on a broad range of policy-authoring tasks.

Phishing attacks, in which criminals lure Internet users to websites that impersonate legitimate sites, are occurring with increasing frequency and are causing considerable harm to victims. In this paper we describe the design and evaluation of an embedded training email system that teaches people about phishing during their normal use of email. We conducted lab experiments contrasting the effectiveness of standard security notices about phishing with two embedded training designs we developed. We found that embedded training works better than the current practice of sending security notices. We also derived sound design principles for embedded training systems.

Grey is a smartphone-based system by which a user can exercise her authority to gain access to rooms in our university building, and by which she can delegate that authority to other users. We present findings from a trial of Grey, with emphasis on how common usability principles manifest themselves in a smartphone-based security application. In particular, we demonstrate aspects of the system that gave rise to failures, misunderstandings, misperceptions, and unintended uses; network effects and new flexibility enabled by Grey; and the implications of these for user behavior. We argue that the manner in which usability principles emerged in the context of Grey can inform the design of other such applications.

In this paper we describe the design and evaluation of Anti-Phishing Phil, an online game that teaches users good habits to help them avoid phishing attacks. We used learning science principles to design and iteratively refine the game. We evaluated the game through a user study: participants were tested on their ability to identify fraudulent web sites before and after spending 15 minutes engaged in one of three anti-phishing training activities (playing the game, reading an anti-phishing tutorial we created based on the game, or reading existing online training materials). We found that the participants who played the game were better able to identify fraudulent web sites compared to the participants in other conditions. We attribute these effects to both the content of the training messages presented in the game as well as the presentation of these materials in an interactive game format. Our results confirm that games can be an effective way of educating people about phishing and other security attacks.

Phishing is a significant problem involving fraudulent email and web sites that trick unsuspecting users into revealing private information. In this paper, we present the design, implementation, and evaluation of CANTINA, a novel, content-based approach to detecting phishing web sites, based on the TF-IDF information retrieval algorithm. We also discuss the design and evaluation of several heuristics we developed to reduce false positives. Our experiments show that CANTINA is good at detecting phishing sites, correctly labeling approximately 95% of phishing sites.

Search engines play an important role in helping users find desired content. With the increasing deployment of computerreadable privacy policies encoded using the standard W3C Platform for Privacy Preferences (P3P) format, search engines also have the potential to help users identify web sites that will respect their privacy needs. We conducted a study of the quantity and quality of P3P-encoded privacy policies associated with top-20 search results from three popular search engines. We built a P3P-enabled search engine and used it to gather statistics on P3P adoption as well as the privacy landscape of the Internet as a whole. This search engine makes use of a privacy policy cache that we designed to facilitate fast searches. Using a list of “typical ” search terms taken from AOL users ’ queries, we examined the trends in privacy policies that are returned from queries to the AOL, Google, and Yahoo! search engines. We then compared these results to results compiled after using “e-commerce search terms ” from Google’s Froogle service. We examined the top 20 search results returned by each search engine for each of the search terms and found at least one result with a P3P policy for 83 % of the typical search terms. Overall we found that these typical search terms yielded P3P adoption rates of 10%. This contrasts with adoption rates of 21 % percent when searching for e-commerce terms. Examining the content of the policies, we discovered that a minority of sites engage in direct marketing with or without a way of opting out, and that even fewer sites share personal information with other companies. Finally, we outline ways to increase P3P adoption rates as well as decrease policy errors.

In high-tech businesses ranging from Internet service providers to e-commerce websites and music stores like Apple iTun-es, there is considerable potential for collecting personal information about customers, monitoring their usage habits, or even exerting control over their behavior - for example, restricting what can be done with a purchased song. A privacy ceiling is an effective limit to these privacy intrusions, created by the perceived or actual legal liability of possessing too much information or control. As we show in this paper, the risk is not simply that of customer backlash, but liability for a customer's actions, owing to the ability to identify, report, or prevent them from taking those actions. In some cases high-tech businesses have been obligated to divulge their store of personal information or to police their customers at the demand of third parties; this unwanted result derives from the possession of too much information or control for the company's own good. We argue that vicarious infringement liability in particular creates a privacy ceiling, a point beyond which there is no economic incentive to intrude on a user's privacy; and, indeed, there is an incentive to architect one's business so that such intrusions are difficult or impossible.

Trust is an important component of online transactions. The increasing amount and sophistication of spam, phishing, and other semantic attacks increase users' uncertainty about the consequences of their actions and their distrust towards other online parties. In this paper, we highlight some key characteristics of a model that we are developing to represent and compare the online trust decision processes of "expert" and "non-expert" computer users. We also report on preliminary data we are gathering to validate, refine, and apply our model. This research is part of a broader project that aims at developing tools and training modules to help online users make good trust decisions.

Phishing emails are semantic attacks that con people into divulging sensitive information using techniques to make the user believe that information is being requested by a legitimate source. In order to develop tools that will be effective in combating these schemes, we first must know how and why people fall for them. This study reports preliminary analysis of interviews with 20 non-expert computer users to reveal their strategies and understand their decisions when encountering possibly suspicious emails. One of the reasons that people may be vulnerable to phishing schemes is that awareness of the risks is not linked to perceived vulnerability or to useful strategies in identifying phishing emails. Rather, our data suggest that people can manage the risks that they are most familiar with, but don't appear to extrapolate to be wary of unfamiliar risks. We explore several strategies that people use, with varying degrees of success, in evaluating emails and in making sense of warnings offered by browsers attempting to help users navigate the web.

While Internet users claim to be concerned about online privacy, their behavior rarely reflects those concerns. In this paper we investigate whether the availability of comparison information about the privacy practices of online merchants affects users’ behavior. We conducted our study using Privacy Finder, a “privacy-enhanced search engine” that displays search results annotated with the privacy policy information of each site. The privacy information is garnered from computer-readable privacy policies found at the respective sites. We asked users to purchase one nonprivacy- sensitive item and then one privacy-sensitive item using Privacy Finder, and observed whether the privacy information provided by our search engine impacted users’ purchasing decisions (participants’ costs were reimbursed, in order to separate the effect of privacy policies from that of price). A control group was asked to make the same purchases using a search engine that produced the same results as Privacy Finder, but did not display privacy information. We found that while Privacy Finder had some influence on non-privacy-sensitive purchase decisions, it had a more significant impact on privacy-sensitive purchases. The results suggest that when privacy policy comparison information is readily available, individuals may be willing to seek out more privacy friendly web sites and perhaps even pay a premium for privacy depending on the nature of the items to be purchased.

Textual passwords are often the only mechanism used to authenticate users of a networked system. Unfortunately, many passwords are easily guessed or cracked. In an attempt to strengthen passwords, some systems instruct users to create mnemonic phrase-based passwords. A mnemonic password is one where a user chooses a memorable phrase and uses a character (often the first letter) to represent each word in the phrase. In this paper, we hypothesize that users will select mnemonic phrases that are commonly available on the Internet, and that it is possible to build a dictionary to crack mnemonic phrase-based passwords. We conduct a survey to gather user-generated passwords. We show the majority of survey respondents based their mnemonic passwords on phrases that can be found on the Internet, and we generate a mnemonic password dictionary as a proof of concept. Our 400,000-entry dictionary cracked 4% of mnemonic passwords; in comparison, a standard dictionary with 1.2 million entries cracked 11% of control passwords. The usergenerated mnemonic passwords were also slightly more resistant to brute force attacks than control passwords. These results suggest that mnemonic passwords may be appropriate for some uses today. However, mnemonic passwords could become more vulnerable in the future and should not be treated as a panacea.

Most people do not often read privacy policies because they tend to be long and difficult to understand. The Platform for Privacy Preferences (P3P) addresses this problem by providing a standard machine-readable format for website privacy policies. P3P user agents can fetch P3P privacy policies automatically, compare them with a user's privacy preferences, and alert and advise the user. Developing user interfaces for P3P user agents is challenging for several reasons: privacy policies are complex, user privacy preferences are often complex and nuanced, users tend to have little experience articulating their privacy preferences, users are generally unfamiliar with much of the terminology used by privacy experts, users often do not understand the privacy-related consequences of their behavior, and users have differing expectations about the type and extent of privacy policy information they would like to see. We developed a P3P user agent called Privacy Bird. Our design was informed by privacy surveys and our previous experience with prototype P3P user agents. We describe our design approach, compare it with the approach used in other P3P use agents, evaluate our design, and make recommendations to designers of other privacy agents.

Internet users now rely on a whole arsenal of tools to protect their security and privacy. Experts recommend that computer users install personal firewalls, anti-virus software, spyware blockers, spam filters, cookie managers, and a variety of other tools to keep themselves safe. Users are told to pick hard-to-guess passwords, use a different password at every Web site, and not to write any of their passwords down. They are told to read privacy policies before providing personal information to Web sites, look for lock icons before typing in a credit card number, refrain from opening email attachments from people they don't know, and even to think twice about opening email attachments from people they do know. With so many do's and don'ts, it is not surprising that much of this advice is ignored. In this talk I will highlight usability problems that make it difficult for people to protect their privacy and security on the Web, and I will discuss a number of approaches to addressing these problems.

The Platform for Privacy Preferences (P3P) provides a standard computer-readable format for privacy policies and a protocol that enables web browsers to read and process these policies automatically. We developed software to query a set of web sites for P3P policies, check the validity of each policy, and analyze the information practices it describes. We used this software to analyze 588 P3P-enabled web sites found by checking for P3P policies on 5,856 web sites on 17 July 2003. The sites we checked for P3P policies were taken from several lists of popular web sites, as well as from "crawling" indexes of shopping, news, children's and government web sites. We present the first major analysis of the data practices of P3P-enabled web sites.

Unauthorized copying of movies is a major concern for the motion picture industry. While unauthorized copies of movies have been distributed via portable physical media for some time, low-cost, high-bandwidth Internet connections and peer-to-peer file sharing networks provide highly efficient distribution media. Many movies are showing up on file sharing networks shortly after, and in some cases prior to, theatrical release. It has been argued that the availability of unauthorized copies directly affects theater attendance and DVD sales, and hence represents a major financial threat to the movie industry. Our research attempts to determine the source of unauthorized copies by studying the availability and characteristics of recent popular movies in file sharing networks. We developed a data set of 312 popular movies and located one or more samples of 183 of these movies on file sharing networks, for a total of 285 movie samples. 77% of these samples appear to have been leaked by industry insiders. Most of our samples appeared on file sharing networks prior to their official consumer DVD release date. Indeed, of the movies that had been released on DVD as of the time of our study, only 5% first appeared after their DVD release date on a web site that indexes file sharing networks, indicating that consumer DVD copying currently represents a relatively minor factor compared with insider leaks. We perform a brief analysis of the movie production and distribution process and identify potential security vulnerabilities that may lead to unauthorized copies becoming available to those who may wish to redistribute them. Finally, we offer recommendations for reducing security vulnerabilities in the movie production and distribution process.

The Internet in its present form does not protect content from censorship. It is straightforward to trace any document back to a specific Web server, and usually directly to an individual. As we discuss below, there are valid reasons for publishing a document in a censorship-resistant manner. Unfortunately, few tools exist that facilitate this form of publishing. We describe the architecture of robust systems for publishing content on the Web. The discussion is in the context of Publius, as that system meets the most design goals of currently deployed systems. Publius has the property that it is very difficult for any adversary to censor or modify the content. In addition, the identity of the publisher is protected once the content is posted. The system differs from others in that tools are provided for updating or deleting published content, and users can browse the content in the normal point-and-click manner using a standard Web browser and a client-side proxy.

Privacy is a necessary concern in electronic commerce.It is difficult, if not impossible, to complete a transaction without revealing some personal data -a shipping address, billing information, or product preference.Users may be unwilling to provide this necessary information or even to browse online if they believe their privacy is invaded or threatened.Fortunately, there are technologies to help users protect their privacy.P3P (Platform for Privacy Preferences Project) from the World Wide Web Consortium is one such technology.However, there is a need to know more about the range of user concerns and preferences about privacy in order to build usable and effective interface mechanisms for P3P and other privacy technologies.Accordingly, we conducted a survey of 381 U.S. Net users, detailing a range of commerce scenarios and examining the participants' concerns and preferences about privacy.This paper presents both the findings from that study as well as their design implications.