Search for: All records

People value their privacy but often lack the time to read privacy policies. This issue is exacerbated in the context of mobile apps, given the variety of data they collect and limited screen space for disclosures. Privacy nutrition labels have been proposed to convey data practices to users succinctly, obviating the need for them to read a full privacy policy. In fall 2020, Apple introduced privacy labels for mobile apps, but research has shown that these labels are ineffective, partly due to their complexity, confusing terminology, and suboptimal information structure. We propose a new design for mobile app privacy labels that addresses information layout challenges by representing data collection and use in a color-coded, expandable grid format. We conducted a between-subjects user study with 200 Prolific participants to compare user performance when viewing our new label against the current iOS label. Our findings suggest that our design significantly improves users' ability to answer key privacy questions and reduces the time required for them to do so. 

Understanding and managing data privacy in the digital world can be challenging for sighted users, let alone blind and lowvision (BLV) users. There is limited research on how BLV users, who have special accessibility needs, navigate data privacy, and how potential privacy tools could assist them. We conducted an in-depth qualitative study with 21 US BLV participants to understand their data privacy risk perception and mitigation, as well as their information behaviors related to data privacy. We also explored BLV users’ attitudes towards potential privacy question answering (Q&A) assistants that enable them to better navigate data privacy information. We found that BLV users face heightened security and privacy risks, but their risk mitigation is often insufficient. They do not necessarily seek data privacy information but clearly recognize the benefits of a potential privacy Q&A assistant. They also expect privacy Q&A assistants to possess cross-platform compatibility, support multi-modality, and demonstrate robust functionality. Our study sheds light on BLV users’ expectations when it comes to usability, accessibility, trust and equity issues regarding digital data privacy. 

People value their privacy but often lack the time to read privacy policies. This issue is exacerbated in the context of mobile apps, given the variety of data they collect and limited screen space for disclosures. Privacy nutrition labels have been proposed to convey data practices to users succinctly, obviating the need for them to read a full privacy policy. In fall 2020, Apple introduced privacy labels for mobile apps, but research has shown that these labels are ineffective, partly due to their complexity, confusing terminology, and suboptimal in- formation structure. We propose a new design for mobile app privacy labels that addresses information layout challenges by representing data collection and use in a color-coded, expand- able grid format. We conducted a between-subjects user study with 200 Prolific participants to compare user performance when viewing our new label against the current iOS label. Our findings suggest that our design significantly improves users’ ability to answer key privacy questions and reduces the time required for them to do so. 

This repository archives the supplemental materials for the USENIX Security '24 paper of the same title. 

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.