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.

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.

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.

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

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