While most people claim to be very concerned about their privacy, they do not consistently take actions to protect it. Web retailers detail their information practices in their privacy policies, but most of the time this information remains invisible to consumers. This paper reports on research undertaken to determine whether a more prominent display of privacy information will cause consumers to incorporate privacy considerations into their online purchasing decisions. We designed an experiment in which a shopping search engine interface, Privacy Finder, clearly displays privacy policy information provided by retailers in a machinereadable format. Privacy Finder annotates search results with a "privacy icon" and a "privacy report." The privacy icon provides a privacy rating for the retailer on a five-point scale. The privacy report summarizes information contained in traditional privacy policies in a short, concise format. Our research shows that providing accessible privacy information reduces the information asymmetry gap between merchants and consumers. This reduction tends to lead consumers to purchase from online retailers who better protect their privacy. Additionally, our study indicates that once privacy information is made more salient, some consumers are willing to pay a premium to purchase from more privacy protective websites.

All four of the most popular webmail providers -AOL, Google, Microsoft, and Yahoo! -rely on personal questions as the secondary authentication secrets used to reset account passwords. The security of these questions has received limited formal scrutiny, almost all of which predates webmail. We ran a user study to measure the reliability and security of the questions used by all four webmail providers. We asked participants to answer these questions and then asked their acquaintances to guess their answers. Acquaintances with whom participants reported being unwilling to share their webmail passwords were able to guess 17% of their answers. Participants forgot 20% of their own answers within six months. What's more, 13% of answers could be guessed within five attempts by guessing the most popular answers of other participants, though this weakness is partially attributable to the geographic homogeneity of our participant pool.

As devices with always-on microphones located in people’s homes, smart speakers have significant privacy implications. We surveyed smart speaker owners about their beliefs, attitudes, and concerns about the recordings that are made and shared by their devices. To ground participants’ responses in concrete interactions, rather than collecting their opinions abstractly, we framed our survey around randomly selected recordings of saved interactions with their devices. We surveyed 116 owners of Amazon and Google smart speakers and found that almost half did not know that their recordings were being permanently stored and that they could review them; only a quarter reported reviewing interactions, and very few had ever deleted any. While participants did not consider their own recordings especially sensitive, they were more protective of others’ recordings (such as children and guests) and were strongly opposed to use of their data by third parties or for advertising. They also considered permanent retention, the status quo, unsatisfactory. Based on our findings, we make recommendations for more agreeable data retention policies and future privacy controls.

Abstract-Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.