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  1. As remote work and learning increases in popularity, individuals, especially those with hearing impairments or who speak English as a second language, may depend on automated transcriptions to participate in business, school, entertainment, or basic communication. In this work, we investigate the automated transcription accuracy of seven popular social media and videoconferencing platforms with respect to some personal characteristics of their users, including gender, age, race, first language, speech rate, F0 frequency, and speech readability. We performed this investigation on a new corpus of 194 hours of English monologues by 846 TED talk speakers. Our results show the presence of significant bias, with transcripts less accurate for speakers that are male or non-native English speakers. We also observe differences in accuracy among platforms for different types of speakers. These results indicate that, while platforms have improved their automatic captioning, much work remains to make captions accessible for a wider variety of speakers and listeners.

     
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    Free, publicly-accessible full text available May 31, 2025
  2. Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial services have become available (IoT safeguards). The safeguards claim to provide protection against IoT privacy risks and security threats. However, the effectiveness and the associated privacy risks of these safeguards remains a key open question. In this paper, we investigate the threat detection capabilities of IoT safeguards for the first time. We develop and release an approach for automated safeguards experimentation to reveal their response to common security threats and privacy risks. We perform thousands of automated experiments using popular commercial IoT safeguards when deployed in a large IoT testbed. Our results indicate not only that these devices may be ineffective in preventing risks, but also their cloud interactions and data collection operations may introduce privacy risks for the households that adopt them. 
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  3. Internet-of-Things (IoT) devices are ubiquitous, but little attention has been paid to how they may incorporate dark patterns despite consumer protections and privacy concerns arising from their unique access to intimate spaces and always-on capabilities. This paper conducts a systematic investigation of dark patterns in 57 popular, diverse smart home devices. We update manual interaction and annotation methods for the IoT context, then analyze dark pattern frequency across device types, manufacturers, and interaction modalities. We find that dark patterns are pervasive in IoT experiences, but manifest in diverse ways across device traits. Speakers, doorbells, and camera devices contain the most dark patterns, with manufacturers of such devices (Amazon and Google) having the most dark patterns compared to other vendors. We investigate how this distribution impacts the potential for consumer exposure to dark patterns, discuss broader implications for key stakeholders like designers and regulators, and identify opportunities for future dark patterns study. 
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  4. Networks today increasingly support in-network functionality via network function virtualization (NFV) or similar technologies. Such approaches enable a wide range of functionality to be deployed on behalf of end systems, such as offloading Tor services, enforcing network usage policies on encrypted traffic, or new functionality in 5G. An important open problem with such approaches is auditing. Namely, such services rely on third-party network providers to faithfully deploy and run their functionality as intended, but often have little to no insight as to whether providers do so. To address this problem, prior work provides point solutions such as verifiable routing with per-packet overhead, or audits of security practices; however, these approaches are not flexible---they are limited to auditing a small set of functionality and do not allow trade-offs between auditing coverage and overhead. In this paper, we propose NFAudit, which allows auditing of deployed NFs with a flexible approach where a wide range of important properties can be audited with configurable, low overhead. Our key insight is that the design of simple, composable, and flexible auditing primitives, combined with limited trust (in the form of secure enclaves) can permit a wide range of auditing functionality and configurable---and often low---cost. 
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