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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. With an increasing number of Internet of Things (IoT) devices present in homes, there is a rise in the number of potential infor- mation leakage channels and their associated security threats and privacy risks. Despite a long history of attacks on IoT devices in unprotected home networks, the problem of accurate, rapid detection and prevention of such attacks remains open. Many existing IoT protection solutions are cloud-based, sometimes ineffective, and might share consumer data with unknown third parties. This paper investigates the potential for effective IoT threat detection locally, on a home router, using AI tools combined with classic rule-based traffic-filtering algorithms. Our results show that with a slight rise of router hardware resources caused by machine learn- ing and traffic filtering logic, a typical home router instrumented with our solution is able to effectively detect risks and protect a typical home IoT network, equaling or outperforming existing popular solutions, with- out any effects on benign IoT functionality, and without relying on cloud services and third parties. 
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    Free, publicly-accessible full text available March 20, 2025
  3. Bluetooth-based item trackers have sparked apprehension over their potential misuse in harmful stalking and privacy violations. In response, manufacturers have implemented safety alerts to notify victims of extended tracking by unknown item trackers. In this study, we specifically investigate the anti-stalking mechanism of Apple's AirTag. We identify and analyze potential triggers of safety alerts that have not been examined in previous research, such as the local time, the victim's device model, AirTag's battery life, and the distance between the AirTag and the victim's device. Furthermore, we demonstrate a novel possibility of developing a stealthy cloned AirTag capable of tracking victims directly on the Find My app while circumventing safety alerts on the victim’s device. Our experiments demonstrate that, despite regular updates to the public key and MAC address, our cloned AirTag can provide real-time location updates even with a four months old key, thereby highlighting the challenges in designing a robust anti-stalking framework. Furthermore, we propose practical solutions to mitigate stalking risks from cloned AirTags and enhance the existing anti-stalking safeguards for AirTags. These suggestions seek to provide a foundation for similar Bluetooth-based item trackers to improve their anti-stalking protections while ensuring optimal tracking efficiency. We conducted rigorous experiments to validate our findings, ensuring their accuracy and reliability. Our evaluation highlights that safety alerts take over 8 hours to appear during the day and are more prompt during the night, particularly after 11 pm.

     
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  4. Targeted advertising remains an important part of the free web browsing experience, where advertisers' targeting and personalization algorithms together find the most relevant audience for millions of ads every day. However, given the wide use of advertising, this also enables using ads as a vehicle for problematic content, such as scams or clickbait. Recent work that explores people's sentiments toward online ads, and the impacts of these ads on people's online experiences, has found evidence that online ads can indeed be problematic. Further, there is the potential for personalization to aid the delivery of such ads, even when the advertiser targets with low specificity. In this paper, we study Facebook--one of the internet's largest ad platforms--and investigate key gaps in our understanding of problematic online advertising: (a) What categories of ads do people find problematic? (b) Are there disparities in the distribution of problematic ads to viewers? and if so, (c) Who is responsible--advertisers or advertising platforms? To answer these questions, we empirically measure a diverse sample of user experiences with Facebook ads via a 3-month longitudinal panel. We categorize over 32,000 ads collected from this panel (n = 132); and survey participants' sentiments toward their own ads to identify four categories of problematic ads. Statistically modeling the distribution of problematic ads across demographics, we find that older people and minority groups are especially likely to be shown such ads. Further, given that 22% of problematic ads had no specific targeting from advertisers, we infer that ad delivery algorithms (advertising platforms themselves) played a significant role in the biased distribution of these ads. 
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  5. Bluetooth-based item trackers have sparked apprehension over their potential misuse in harmful stalking and privacy violations. In response, manufacturers have implemented safety alerts to notify victims of extended tracking by unknown item trackers. In this study, we specifically investigate the anti-stalking mechanism of Apple's AirTag. We identify and analyze potential triggers of safety alerts that have not been examined in previous research, such as the local time, the victim's device model, AirTag's battery life, and the distance between the AirTag and the victim's device. Furthermore, we demonstrate a novel possibility of developing a stealthy cloned AirTag capable of tracking victims directly on the Find My app while circumventing safety alerts on the victim’s device. Our experiments demonstrate that, despite regular updates to the public key and MAC address, our cloned AirTag can provide real-time location updates even with a four months old key, thereby highlighting the challenges in designing a robust anti-stalking framework. Furthermore, we propose practical solutions to mitigate stalking risks from cloned AirTags and enhance the existing anti-stalking safeguards for AirTags. These suggestions seek to provide a foundation for similar Bluetooth-based item trackers to improve their anti-stalking protections while ensuring optimal tracking efficiency. We conducted rigorous experiments to validate our findings, ensuring their accuracy and reliability. Our evaluation highlights that safety alerts take over 8 hours to appear during the day and are more prompt during the night, particularly after 11 pm. 
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  6. 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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  7. 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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