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1. Privacy Controls for Always-Listening Devices

   https://doi.org/10.1145/3368860.3368867  

   Malkin, Nathan; Egelman, Serge; Wagner, David (September 2019, New Security Paradigms Workshop)

   Intelligent voice assistants (IVAs) and other voice-enabled devices already form an integral component of the Internet of Things and will continue to grow in popularity. As their capabilities evolve, they will move beyond relying on the wake-words today's IVAs use, engaging instead in continuous listening. Though potentially useful, the continuous recording and analysis of speech can pose a serious threat to individuals' privacy. Ideally, users would be able to limit or control the types of information such devices have access to. But existing technical approaches are insufficient for enforcing any such restrictions. To begin formulating a solution, we develop a systematic methodology for studying continuous-listening applications and survey architectural approaches to designing a system that enhances privacy while preserving the benefits of always-listening assistants. 

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2. StretchAR: Exploiting Touch and Stretch as a Method of Interaction for Smart Glasses Using Wearable Straps

   https://doi.org/10.1145/3550305  

   Paredes, Luis; Ipsita, Ananya; Mesa, Juan C.; Martinez Garrido, Ramses V.; Ramani, Karthik (September 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Over the past decade, augmented reality (AR) developers have explored a variety of approaches to allow users to interact with the information displayed on smart glasses and head-mounted displays (HMDs). Current interaction modalities such as mid-air gestures, voice commands, or hand-held controllers provide a limited range of interactions with the virtual content. Additionally, these modalities can also be exhausting, uncomfortable, obtrusive, and socially awkward. There is a need to introduce comfortable interaction techniques for smart glasses and HMDS without the need for visual attention. This paper presents StretchAR, wearable straps that exploit touch and stretch as input modalities to interact with the virtual content displayed on smart glasses. StretchAR straps are thin, lightweight, and can be attached to existing garments to enhance users' interactions in AR. StretchAR straps can withstand strains up to 190% while remaining sensitive to touch inputs. The strap allows the effective combination of these inputs as a mode of interaction with the content displayed through AR widgets, maps, menus, social media, and Internet of Things (IoT) devices. Furthermore, we conducted a user study with 15 participants to determine the potential implications of the use of StretchAR as input modalities when placed on four different body locations (head, chest, forearm, and wrist). This study reveals that StretchAR can be used as an efficient and convenient input modality for smart glasses with a 96% accuracy. Additionally, we provide a collection of 28 interactions enabled by the simultaneous touch-stretch capabilities of StretchAR. Finally, we facilitate recommendation guidelines for the design, fabrication, placement, and possible applications of StretchAR as an interaction modality for AR content displayed on smart glasses. 

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3. Bystander Privacy in Smart Homes: A Systematic Review of Concerns and Solutions

   https://doi.org/10.1145/3731755  

   Saqib, Eimaan; He, Shijing; Choy, Junghyun; Abu-Salma, Ruba; Such, Jose; Bernd, Julia; Javed, Mobin (October 2025, ACM Transactions on Computer-Human Interaction)

   Hornbæk, Kasper (Ed.)

   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. 

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4. Tangible Privacy for Smart Voice Assistants: Bystanders' Perceptions of Physical Device Controls

   https://doi.org/10.1145/3555089  

   Ahmad, Imtiaz; Akter, Taslima; Buher, Zachary; Farzan, Rosta; Kapadia, Apu; Lee, Adam J. (November 2022, Proceedings of the ACM on Human-Computer Interaction)

   Smart voice assistants such as Amazon Alexa and Google Home are becoming increasingly pervasive in our everyday environments. Despite their benefits, their miniaturized and embedded cameras and microphones raise important privacy concerns related to surveillance and eavesdropping. Recent work on the privacy concerns of people in the vicinity of these devices has highlighted the need for 'tangible privacy', where control and feedback mechanisms can provide a more assured sense of whether the camera or microphone is 'on' or 'off'. However, current designs of these devices lack adequate mechanisms to provide such assurances. To address this gap in the design of smart voice assistants, especially in the case of disabling microphones, we evaluate several designs that incorporate (or not) tangible control and feedback mechanisms. By comparing people's perceptions of risk, trust, reliability, usability, and control for these designs in a between-subjects online experiment (N=261), we find that devices with tangible built-in physical controls are perceived as more trustworthy and usable than those with non-tangible mechanisms. Our findings present an approach for tangible, assured privacy especially in the context of embedded microphones. 

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5. Spying through Your Voice Assistants: Realistic Voice Command Fingerprinting

   Ahmed, Dilawer; Sabir, Aafaq; Das, Anupam (August 2023, 32nd USENIX Security Symposium (USENIX Security 23))

   Voice assistants are becoming increasingly pervasive due to the convenience and automation they provide through the voice interface. However, such convenience often comes with unforeseen security and privacy risks. For example, encrypted traffic from voice assistants can leak sensitive information about their users' habits and lifestyles. In this paper, we present a taxonomy of fingerprinting voice commands on the most popular voice assistant platforms (Google, Alexa, and Siri). We also provide a deeper understanding of the feasibility of fingerprinting third-party applications and streaming services over the voice interface. Our analysis not only improves the state-of-the-art technique but also studies a more realistic setup for fingerprinting voice activities over encrypted traffic.Our proposed technique considers a passive network eavesdropper observing encrypted traffic from various devices within a home and, therefore, first detects the invocation/activation of voice assistants followed by what specific voice command is issued. Using an end-to-end system design, we show that it is possible to detect when a voice assistant is activated with 99% accuracy and then utilize the subsequent traffic pattern to infer more fine-grained user activities with around 77-80% accuracy. 

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