More Like this

1. Toggles, Dollar Signs, and Triangles: How to (In)Effectively Convey Privacy Choices

   Habib, H. ; Zou, Y. ; Yao, Y. ; Acquisti, A. ; Cranor, L. ; Reidenberg, J. ; Sadeh, N. ; Schaub, F. ( January 2021 , ACM Conference on Human Factors in Computing Systems)

   Increasingly, icons are being proposed to concisely convey privacyrelated information and choices to users. However, complex privacy concepts can be difcult to communicate. We investigate which icons efectively 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 identifed 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. Themore »two CCPA-mandated link texts (“Do Not Sell My Personal Information” and “Do Not Sell My Info”) accurately communicated the presence of do-notsell 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.« less
2. Finding a Choice in a Haystack: Automatic Extraction of Opt-Out Statements from Privacy Policy Text

   https://doi.org/10.1145/3366423.3380262  

   Bannihatti Kumar, Vinayshekhar ; Iyengar, Roger ; Nisal, Namita ; Feng, Yuanyuan ; Habib, Hana ; Story, Peter ; Cherivirala, Sushain ; Hagan, Margaret ; Cranor, Lorrie ; Wilson, Shomir ; et al ( April 2020 , WWW '20: Proceedings of the Web Conference 2020)

   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 combinesmore »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.« less
3. “Did you know this camera tracks your mood?”: Understanding Privacy Expectations and Preferences in the Age of Video Analytics

   https://doi.org/10.2478/popets-2021-0028  

   Zhang, Shikun ; Feng, Yuanyuan ; Bauer, Lujo ; Cranor, Lorrie Faith ; Das, Anupam ; Sadeh, Norman ( January 2021 , Proceedings on Privacy 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,more »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.« less
4. Did You Know This Camera Tracks Your Mood? Modeling People’s Privacy Expectations and Preferences in the Age of Video Analytics

   Zhang, S. ; Feng, Y. ; Bauer, L ; Cranor, L. ; Das, A. ; Sadeh, N. ( January 2021 , PoPETS)

   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, withmore »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.« less
5. Examining the Literacy Practices of Engineers to Develop a Model of Disciplinary Literacy Instruction for K-12 Engineering (Work in Progress)

   Green, Theresa ; Minichiello, Angela ; Wilson-Lopez, Amy ( June 2018 , ASEE Annual Conference proceedings)

   Despite efforts to diversify the science, technology, engineering, and mathematics (STEM) workforce, engineering remains a White, male-dominated profession. Often, women and underrepresented students do not identify with STEM careers and many opt out of STEM pathways prior to entering high school or college. In order to broaden participation in engineering, new methods of engaging and retaining those who are traditionally underrepresented in engineering are needed. This work is based on a promising approach for encouraging and supporting diverse participation in engineering: disciplinary literacy instruction (DLI). Generally, teachers use DLI to provide K-12 students with a framework for interpreting, evaluating, andmore »generating discipline-specific texts. This instruction provides students with an understanding of how experts in the discipline read, engage, and generate texts used to solve problems or communicate information. While models of disciplinary literacy have been developed and disseminated in several humanities and science fields, there is a lack of empirical and theoretical research that examines the use of DLI within the engineering domain. It is thought that DLI can be used to foster diverse student interest in engineering from a young age by removing literacy-based barriers that often discourage underrepresented students from entering and pursuing careers in STEM fields. This work-in-progress paper describes a new study underway to develop and disseminate a model of disciplinary literacy in engineering. During this project, researchers will observe, interview, and collect written artifacts from engineers working across four sub-disciplines of engineering: aerospace/mechanical, biological, civil/environmental, and electrical/computer. Data that will be collected include interview transcripts, observation field notes, engineer logs of literacy practices, and photographs of texts that the engineers read and write. Data will be analyzed using constant comparative analytic (CCA) methods. CCA will be used to generate theoretical codes from the data that will form the basis for a model of disciplinary literacy in engineering. As a primary outcome of this research, the engineering DLI model will promote the use of DLI practices within K-12 engineering instruction in order to assist and encourage diverse, underrepresented students to engage in engineering courses of study and pursue STEM careers. Thus far, the research team has begun collecting and analyzing data from two electrical engineers. This work in progress paper will report on preliminary findings, as well as implications for K-12 classroom instruction. For instance, this study has shed insights on how engineers use texts as part of the process of conducting failure analysis, and the research team has begun to conceptualize how these types of texts might be used with K-12 students to help them conduct failure analyses during design testing. Ultimately, this project will result in a list of grade-appropriate texts, evaluative frameworks, and activities (e.g., failure analysis in testing) that K-12 engineering teachers can use to prepare their diverse students to think, act, read, and write like engineers.« less