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The U.S. Government is developing a package label to help consumers access reliable security and privacy information about Internet of Things (IoT) devices when making purchase decisions. The label will include the U.S. Cyber Trust Mark, a QR code to scan for more details, and potentially additional information. To examine how label information complexity and educational interventions affect comprehension of security and privacy attributes and label QR code use, we conducted an online survey with 518 IoT purchasers. We examined participants' comprehension and preferences for three labels of varying complexities, with and without an educational intervention. Participants favored and correctly utilized the two higher-complexity labels, showing a special interest in the privacy-relevant content. Furthermore, while the educational intervention improved understanding of the QR code’s purpose, it had a modest effect on QR scanning behavior. We highlight clear design and policy directions for creating and deploying IoT security and privacy labels.
Trust in digital systems depends on secure hardware, often assured through Hardware Reverse Engineering (HRE). This work develops methods for investigating human problem-solving processes in HRE, an underexplored yet critical aspect. Since reverse engineers rely heavily on visual information, eye tracking holds promise for studying their cognitive processes. To gain further insights, we additionally employ verbal thought protocols during and immediately after HRE tasks: Concurrent and Retrospective Think Aloud. We evaluate the combination of eye tracking and Think Aloud with 41 participants in an HRE simulation. Eye tracking accurately identifies fixations on individual circuit elements and highlights critical components. Based on two use cases, we demonstrate that eye tracking and TA can complement each other to improve data quality. Our methodological insights can inform future studies in HRE, a specific setting of human-computer interaction, and in other problem-solving settings involving misleading or missing information.
Client-Side Scanning (CSS) is discussed as a potential solution to contain the dissemination of child sexual abuse material (CSAM). A significant challenge associated with this debate is that stakeholders have different interpretations of the capabilities and frontiers of the concept and its varying implementations. In this paper, we explore stakeholders' understandings of the technology and the expectations and potential implications in the context of CSAM by conducting and analyzing 28 semi-structured interviews with a diverse sample of experts. We identified mental models of CSS and the expected challenges. Our results show that CSS is often a preferred solution in the child sexual abuse debate due to the lack of an alternative. Our findings illustrate the importance of further interdisciplinary discussions to define and comprehend the impact of CSS usage on society, particularly vulnerable groups such as children.
Patient outreach enables timely communication between patients and healthcare providers but is vulnerable to phishing/spoofing attacks. In this paper, we work with a U.S.-based healthcare provider to design an inclusive method to address this threat. We present VeriSMS which allows patients to call a voice agent to verify whether the received (sensitive) messages are indeed sent by their healthcare provider. We design the system to be inclusive: it is accessible to patients who only have access to SMS and phone call capabilities. We perform a two-part user study to refine the system design (N=15) and confirm users can correctly understand the system and use it to identify spoofed/phishing messages (N=35). A key insight from our study is to not exclusively optimize for strong security but to tailor the designs based on user habits. Our result confirms the effectiveness and usability of VeriSMS and its ability to significantly increase adversaries' costs.
This paper investigates the use of through-skull sound conduction to authenticate smartglass users. We mount a surface transducer on the right mastoid process to play cue signals and capture skull-transformed audio responses through contact microphones on various skull locations. We use the resultant bio-acoustic information as classification features. In an initial single-session study (N=25), we achieved mean Equal Error Rates (EERs) of 5.68% and 7.95% with microphones on the brow and left mastoid process. Combining the two signals substantially improves performance (to 2.35% EER). A subsequent multi-session study (N=30) demonstrates EERs are maintained over three recalls and, additionally, shows robustness to donning variations and background noise (achieving 2.72% EER). In a follow-up usability study over one week, participants report high levels of usability (as expressed by SUS scores) and that only modest workload is required to authenticate. Finally, a security analysis demonstrates the system's robustness to spoofing and imitation attacks.