Many devices are accessed and controlled through voice assistants today, a representative example being Echo smart speakers and other Amazon devices controlled by Alexa. These offer the convenience of accessing services through voice interactions, but also raise privacy concerns, as data can be stored and used for personalization, and voice biometric information is sensitive. Unfortunately, there remains a lack of transparency and control over the collection and use of this data. Although prior work has shown evidence of ad targeting based on data derived from voice interactions and user profiles/interests, it has so far been an open question whether voice biometric information itself is utilized for targeting. In this paper, (i) we build a general auditing methodology to answer this question for off-the-shelf commercial smart speakers, and (ii) we apply it specifically to Amazon Echo Dot. Our findings suggest that Amazon Music ad content is more strongly associated with attributes (gender and age) related to voice characteristics than would be expected by chance. This has important implications for compliance, since voice contains sensitive biometric information that is protected by several privacy regulations.

City-wide free WiFi is one of the most common initiatives of smart city infrastructures. While city-wide free WiFi services are not subject to privacy-focused regulations and appeal to a broader demographic, how users perceive privacy in such services is unknown. This study explores perspectives of users in the United States regarding the privacy practices of such services as well as their expectations. We conducted surveys with 199 participants of US, consisting of those who had used such services (i.e., experienced users, n=99) and those who had not (i.e., potential users, n=100), assessing their satisfaction with the services, perceptions regarding data privacy practices of city-wide free WiFi services, and general expectations of privacy. We identify 14 key findings by analyzing the responses from participants. We found that participants are aware of the data collection and data sharing by the WiFi services and are uncomfortable with both but are still inclined to use the services as the need for WiFi outweighs privacy, as well as because of the significant trust they place in the services due to their non-profit and government-run nature. Our analysis provides actionable takeaways for researchers and practitioners, arguing for long-term privacy gains through a regulatory approach that treats city-wide WiFi as a utility, given the trust consumers place in it, and the overall tendency of consumers to trade-off privacy for WiFi access in this context.

Data collection through the Internet of Things (IoT) devices, or smart devices, in commercial buildings enables possibilities for increased convenience and energy efficiency. However, such benefits face a large perceptual challenge when being implemented in practice, due to the different ways occupants working in the buildings understand and trust in the data collection. The semi-public, pervasive, and multi-modal nature of data collection in smart buildings points to the need to study occupants’ understanding of data collection and notification preferences. We conduct an online study with 492 participants in the US who report working in smart commercial buildings regarding: 1) awareness and perception of data collection in smart commercial buildings, 2) privacy notification preferences, and 3) potential factors for privacy notification preferences. We find that around half of the participants are not fully aware of the data collection and use practices of IoT even though they notice the presence of IoT devices and sensors. We also discover many misunderstandings around different data practices. The majority of participants want to be notified of data practices in smart buildings, and they prefer push notifications to passive ones such as websites or physical signs. Surprisingly, mobile app notification, despite being a popular channel for smart homes, is the least preferred method for smart commercial buildings.

Many households include children who use voice personal assistants (VPA) such as Amazon Alexa. Children benefit from the rich functionalities of VPAs and third-party apps but are also exposed to new risks in the VPA ecosystem. In this article, we first investigate “risky” child-directed voice apps that contain inappropriate content or ask for personal information through voice interactions. We build SkillBot—a natural language processing-based system to automatically interact with VPA apps and analyze the resulting conversations. We find 28 risky child-directed apps and maintain a growing dataset of 31,966 non-overlapping app behaviors collected from 3,434 Alexa apps. Our findings suggest that although child-directed VPA apps are subject to stricter policy requirements and more intensive vetting, children remain vulnerable to inappropriate content and privacy violations. We then conduct a user study showing that parents are concerned about the identified risky apps. Many parents do not believe that these apps are available and designed for families/kids, although these apps are actually published in Amazon’s “Kids” product category. We also find that parents often neglect basic precautions, such as enabling parental controls on Alexa devices. Finally, we identify a novel risk in the VPA ecosystem: confounding utterances or voice commands shared by multiple apps that may cause a user to interact with a different app than intended. We identify 4,487 confounding utterances, including 581 shared by child-directed and non-child-directed apps. We find that 27% of these confounding utterances prioritize invoking a non-child-directed app over a child-directed app. This indicates that children are at real risk of accidentally invoking non-child-directed apps due to confounding utterances.

Wasi Ahmad, Jianfeng Chi, Tu Le, Thomas Norton, Yuan Tian, Kai-Wei Chang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

With the rapid development of the Internet of Things (IoT), there are billions of interacting devices and applications. With so many devices and applications, one of the most critical challenges is how to provide security. Traditional software-based defenses will not be enough to protect the security of IoT because of the attack surfaces derived from the physical environment. For example, an attacker can physically re-point a surveillance camera, can move a smart device to another location, can send a sound signal to influence an accelerometer, can cause wireless jamming, etc. We propose to create "smart buttons," and collections of them called "smart blankets" as hardware/software (HW/SW) security patches rather than software-only patches. These fixes operate similarly to software patches, but because of the hardware added, these new patches can better support against physical world attacks. While this paper primarily presents a vision for HW/SW patches, solutions are implemented and shown for two classes of attacks involving cameras and robots. Open questions are also discussed.