Mobile telehealth apps can provide valuable services, but they raise significant privacy and security concerns, as they collect health-related and other sensitive personal information. We conducted two surveys (𝑁 = 1, 256 total) to examine US users’ privacy expectations about Android telehealth and teletherapy apps’ data practices and legal privacy protections for the data they collect, gaps between those expectations and actual practices and protections, and how privacy perceptions and behavioral intentions are affected by privacy disclosures. In Survey 1, we explored participants’ privacy perceptions and intentions about 10 telehealth apps, first after reading just the general description from the Google Play Store, then after reading the Data Safety section (DSS). Survey 2 explored broader privacy expectations regarding telehealth apps, regulatory awareness, and preferred legal protections. Findings indicate that participants perceived apps provided by independent developers as less likely to protect privacy than apps offered by healthcare providers. However, participants often had inaccurate privacy expectations and overestimated legal safeguards, potentially leading to uninformed privacy decisions. DSSs significantly affected participants’ expectations about data practices and legal protections and their likelihood of using the app—but while DSSs often increased participants’ confidence in their privacy expectations, they did not necessarily improve their accuracy.

Current privacy protections for smart home devices rarely consider bystanders' privacy, whose preferences are varied and may differ from primary users. We use Contextual Integrity theory to explore context-dependent variation in privacy norms regarding smart home bystanders’ data. We conducted a vignette-based survey with 761 participants in the US, varying parameter values to capture acceptability judgments regarding bystander information flows in certain situations: domestic work, shared housing, visiting a friend overnight, and Airbnb. We found that recipients and purposes of sharing impact acceptance the most. Sharing interaction logs was more acceptable than audio or video. Sharing smart speaker data was less acceptable than smart camera or smart door lock data. We found nuanced interaction effects between factors in different smart home situations, and differences between protections most favored by participants playing bystander vs. owner roles. We provide design and policy recommendations for smart home privacy protections that consider bystanders' needs.

Smart home devices raise privacy concerns among not only primary users but also bystanders like domestic workers. We conducted 25 qualitative interviews with nannies and 16 with parents who employed nannies, in the U.S., to explore and compare their views on and privacy threat models for smart home devices. We found device-specific purposes of use inspired different perspectives among nanny participants. Most were comfortable with employers’ smart speakers and smart TVs, whose purpose had nothing to do with them. However, with indoor smart cameras, nanny participants were often not just bystanders but targets of monitoring; in such situations, they had a wider range of attitudes. In contrast, parent participants tended to have more similar views across devices. We found notable disconnects regarding disclosure, where nanny participants often hesitated to ask about cameras, but parent participants assumed nannies just didn’t care. We recommend prioritizing interventions supporting disclosure, discussion, and sharing control.

Smart home devices, such as security cameras and voice assistants, have seen widespread adoption due to the utility and convenience they offer to users. The deployment of these devices in homes, however, raises privacy concerns for bystanders—people who may not necessarily have a say in the deployment and configuration of these devices, and yet are exposed to or affected by their data collection. Examples of bystanders include guests, short-term tenants, and domestic workers. Prior work has studied the privacy concerns of different bystander groups and proposed design solutions for addressing these concerns. In this article, we present a systematic review of previous studies, describing how smart home bystanders are defined and classified, and illuminating the range of concerns and solutions proposed in the existing academic literature. We also discuss limitations in prior work, barriers to the uptake of research-based solutions by industry, and identify avenues for future research.

As smart home devices proliferate, protecting the privacy of those who encounter the devices is of the utmost importance both within their own home and in other people's homes. In this study, we conducted a large-scale survey (N=1459) with primary users of and bystanders to smart home devices. While previous work has studied people's privacy experiences and preferences either as smart home primary users or as bystanders, there is a need for a deeper understanding of privacy experiences and preferences in different contexts and across different countries. Instead of classifying people as either primary users or bystanders, we surveyed the same participants across different contexts. We deployed our survey in four countries (Germany, Mexico, the United Kingdom, and the United States) and in two languages (English and Spanish). We found that participants were generally more concerned about devices in their own homes, but perceived video cameras—especially unknown ones—and usability as more concerning in other people's homes. Compared to male participants, female and non-binary participants had less control over configuration of devices and privacy settings—regardless of whether they were the most frequent user. Comparing countries, participants in Mexico were more likely to be comfortable with devices, but also more likely to take privacy precautions around them. We also make cross-contextual recommendations for device designers and policymakers, such as nudges to facilitate social interactions.

Cybersecurity is a growing job field--and even for students who don't go on to pursue cybersecurity careers, it is crucial to have some level of security awareness. But until recently, the intrigue and opportunity of cybersecurity was usually not introduced until advanced undergraduate CS courses. However, the real world implications and applied nature of the topic lends itself well to catching the interest of a diverse group of students in CS at a younger age.

The sharing of information between older adults and their friends, families, caregivers, and doctors promotes a collaborative approach to managing their emotional, mental, and physical well-being and health, prolonging independent living, and improving care quality and quality of life in general. However, information flow in collaborative systems is complex, not always transparent to elderly users, and may raise privacy and security concerns. Because older adults’ decisions about whether to engage in information exchange affect interpersonal communications and delivery of care, it is important to understand the factors and context that influence those decisions. Our work contributes empirical evidence and suggests a systematic approach. In this article, we present the results of semi-structured interviews with 46 older adults aged 65+ about their views on information collection, transmission, and sharing. We develop a detailed model of the contextual factors that combine in complex ways to affect older adults’ decision making about information sharing. We discuss how our comprehensive model compares to existing frameworks for analyzing information-sharing expectations and preferences. Finally, we suggest directions for future research and describe the practical implications of our model for the design and evaluation of collaborative information-sharing systems, as well as for policy and consumer protection.

Cybersecurity is a growing job field -- and even for students who don't go on to pursue cybersecurity careers, it is crucial to have some level of security awareness. But until very recently, the intrigue and opportunity of cybersecurity was usually not introduced until advanced undergraduate CS courses. However, the real world implications and applied nature of the topic lends itself well to catching the interest of a diverse group of students in CS at a younger age.

Cybersecurity is vital to a technology-driven society. Daily headlines about data breaches and ransomware attacks demonstrate that we cannot ignore the security risks inherent to our highly networked lives. Cybersecurity is a growing job field -- and even for students who don't go on to pursue cybersecurity careers, it is crucial to have some level of security awareness. But until very recently, the intrigue and opportunity of cybersecurity was usually not introduced until advanced undergraduate CS courses. However, the real world implications and applied nature of the topic lends itself well to catching the interest of a diverse group of students in CS at a younger age. The Teaching Security lessons (at teachingsecurity.org) introduce the broad idea of cybersecurity through threat modeling and the human-centered nature of authentication. They were prepared by subject-matter experts with research backgrounds in the technical workings and social implications of cybersecurity. Our lessons were designed to meet the cybersecurity learning objectives in the AP Computer Science Principles framework, but they are appropriate for any high school computer science class or program. This will be an interactive workshop for CS educators at all levels; no previous cybersecurity experience required. (Laptops also optional.) Participants will learn how to begin developing the "security mindset" by teaching students a simplified version of threat modeling (mostly via "unplugged" activities). We will also preview lessons on authentication and social engineering. The workshop will also provide opportunities for attendees who teach cybersecurity to share their own strategies.

One of the most innovative features of the new AP Computer Science Principles course framework is that it includes the Global Impact of Computing-not just as an occasional flourish, but as one of its foundational Big Ideas (#7). The real-world impact of computing-on society and on their own lives-is a great hook that can stimulate students' lasting interest in computer science, whether in CSP or any other CS class. Teach Global Impact is a collaborative effort among leading computer science educators that leverages seven excellent CSP curricula and PD programs. (BJC, CISS, Code.org CSP, CS Matters, CSP CS4HS, Mobile CSP, and UTeach CSP.) These curricula are contributing activities that highlight the potential impacts of big data, multimedia, artificial intelligence, and Internet connectivity, among other things. We are working to bring together all of these existing Global Impact lesson materials into a unified resource, and to fill in any gaps by creating new materials, along with classroom strategy guides for teaching about impact. This lightning talk will introduce teachers to the resources available on the Teach Global Impact website, including a database of existing materials, new activities and strategy guides, and a Computing in the News feed featuring student-curated stories aligned with CSP learning objectives. We'll also talk about new activities in the works, including classroom simulations around net neutrality and encryption ethics, a lesson plan on online research and writing for CS, and a series of videos illustrating key essential knowledge points.

This paper presents a novel two-phase method for audio representation, discriminative and compact audio representation (DCAR), and evaluates its performance at detecting events and scenes in consumer-produced videos. In the first phase of DCAR, each audio track is modeled using a Gaussian mixture model (GMM) that includes several components to capture the variability within that track. The second phase takes into account both global structure and local structure. In this phase, the components are rendered more discriminative and compact by formulating an optimization problem on a Grassmannian manifold. The learned components can effectively represent the structure of audio. Our experiments used the YLI-MED and DCASE Acoustic Scenes datasets. The results show that variants on the proposed DCAR representation consistently outperform four popular audio representations (mv-vector, i-vector, GMM, and HEM-GMM). The advantage is significant for both easier and harder discrimination tasks; we discuss how these performance differences across tasks follow from how each type of model leverages (or does not leverage) the intrinsic structure of the data.

This tutorial brings together a number of recent advances at the nexus of multimedia analysis, online privacy, and social media mining. Our goal is to offer a multidisciplinary view of the emerging field of Multimedia Privacy: the study of privacy issues arising in the context of multimedia sharing in online platforms, and the pursuit of new approaches to mitigating those issues within multimedia computer science.

This paper presents a novel two-phase method for audio representation: Discriminative and Compact Audio Representation (DCAR). In the first phase, each audio track is modeled using a Gaussian mixture model (GMM) that includes several components to capture the variability within that track. The second phase takes into account both global structure and local structure. In this phase, the components are rendered more discriminative and compact by formulating an optimization problem on Grassmannian manifolds, which we found represents the structure of audio effectively. Experimental results on the YLI-MED dataset show that the proposed DCAR representation consistently outperforms state-of-the-art audio representations: i-vector, mv-vector, and GMM.

Leveraged wisely, new datasets can inspire new multimedia methods and algorithms, as well as catalyze innovations in how their efficacy, efficiency, and generalizability can be evaluated. The availability of very large multimedia datasets like the Yahoo-Flickr Creative Commons 100 Million has offered unique opportunities for advancing the state of the art in multimedia processing, analysis, search, and visualization. The Multimedia Commons Initiative has been developing a community around the YFCC100M, including associated annotation and evaluation efforts. In addition to research in several multimedia subfields, including computer vision, image processing, and video content analysis, the YFCC100M and Multimedia Commons resources have been used in various competitions and benchmarks, such as the MediaEval Placing Task and the ACM Multimedia Grand Challenge competition. With additional annotation and curation, the data has the potential to enable major leaps forward in research.

A basic understanding of online privacy is essential to being an informed digital citizen, and therefore basic privacy education is becoming ever more necessary. Recently released high school and college computer science curricula acknowledge the significantly increased importance of fundamental knowledge about privacy, but do not yet provide concrete content in the area. To address this need, over the past two years, we have developed the Teaching Privacy Project (TPP) curriculum, http://teachingprivacy.org, which educates the general public about online privacy issues. We performed a pilot of our curriculum in a university course for non-CS majors and found that it was effective: weeks after last being exposed, students' privacy attitudes had shifted. In this paper, we describe our curriculum, our evaluation of it in the classroom, and our vision for future privacy education.

This paper presents advances in analyzing audio content information to detect events in videos, such as a parade or a birthday party. We developed a set of tools for audio processing within the predominantly vision-focused deep neural network (DNN) framework Caffe. Using these tools, we show, for the first time, the potential of using only a DNN for audio-based multimedia event detection. Training DNNs for event detection using the entire audio track from each video causes a computational bottleneck. Here, we address this problem by developing a sparse audio frame-sampling method that improves event-detection speed and accuracy. We achieved a 10 percentage-point improvement in event classification accuracy, with a 200x reduction in the number of training input examples as compared to using the entire track. This reduction in input feature volume led to a 16x reduction in the size of the DNN architecture and a 300x reduction in training time. We applied our method using the recently released YLI-MED dataset and compared our results with a state-of-the-art system and with results reported in the literature for TRECVIDMED. Our results show much higher MAP scores compared to a baseline i-vector system - at a significantly reduced computational cost. The speed improvement is relevant for processing videos on a large scale, and could enable more effective deployment in mobile systems.