Smart home cameras raise privacy concerns in part because they frequently collect data not only about the primary users who deployed them but also other parties -- who may be targets of intentional surveillance or incidental bystanders. Domestic employees working in smart homes must navigate a complex situation that blends privacy and social norms for homes, workplaces, and caregiving.
This paper presents findings from 25 semi-structured interviews with domestic childcare workers in the U.S. about smart home cameras, focusing on how privacy considerations interact with the dynamics of their employer-employee relationships. We show how participants’ views on camera data collection, and their desire and ability to set conditions on data use and sharing, were affected by power differentials and norms about who should control information flows in a given context. Participants’ attitudes about employers’ cameras often hinged on how employers used the data; whether participants viewed camera use as likely to reinforce negative tendencies in the employer-employee relationship; and how camera use and disclosure might reflect existing relationship tendencies.
We also suggest technical and social interventions to mitigate the adverse effects of power imbalances on domestic employees’ privacy and individual agency.
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Critical-Playful Speculations with Cameras in the Home
Smart home cameras present new challenges for understanding behaviors and relationships surrounding always-on, domestic recording systems. We designed a series of discursive activities involving 16 individuals from ten households for six weeks in their everyday settings. These activities functioned as speculative probes prompting participants to reflect on themes of privacy and power through filming with cameras in their households. Our research design foregrounded critical-playful enactments that allowed participants to speculate potentials for relationships with cameras in the home beyond everyday use. We present four key dynamics with participants and home cameras by examining their relationships to: the camera’s eye, filming, their data, and camera’s societal contexts. We contribute discussions about the mundane, information privacy, and post-hoc reflection with one’s camera footage. Overall, our findings reveal the camera as a strange, yet banal entity in the home—interrogating how participants compose and handle their own and others’ video data.
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- Award ID(s):
- 1947696
- NSF-PAR ID:
- 10349157
- Date Published:
- Journal Name:
- Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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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Many consumer Internet Things (IoT) devices involve spatial sensors such as cameras and microphones. These affect the privacy of nearby people. A prime example is smart home security cameras. We present our work developing scenarios, use cases, and design proposals for addressing smart camera privacy. Preliminary findings from a concept evaluation with 11 participants is presented. The outcomes of this research through design project foreground the importance and challenges of designing to support the privacy of nearby users. We outline actionable design responses while also raising limitations of technology approaches alone to address these issues.more » « less
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The use of audio and video modalities for Human Activity Recognition (HAR) is common, given the richness of the data and the availability of pre-trained ML models using a large corpus of labeled training data. However, audio and video sensors also lead to significant consumer privacy concerns. Researchers have thus explored alternate modalities that are less privacy-invasive such as mmWave doppler radars, IMUs, motion sensors. However, the key limitation of these approaches is that most of them do not readily generalize across environments and require significant in-situ training data. Recent work has proposed cross-modality transfer learning approaches to alleviate the lack of trained labeled data with some success. In this paper, we generalize this concept to create a novel system called VAX (Video/Audio to 'X'), where training labels acquired from existing Video/Audio ML models are used to train ML models for a wide range of 'X' privacy-sensitive sensors. Notably, in VAX, once the ML models for the privacy-sensitive sensors are trained, with little to no user involvement, the Audio/Video sensors can be removed altogether to protect the user's privacy better. We built and deployed VAX in ten participants' homes while they performed 17 common activities of daily living. Our evaluation results show that after training, VAX can use its onboard camera and microphone to detect approximately 15 out of 17 activities with an average accuracy of 90%. For these activities that can be detected using a camera and a microphone, VAX trains a per-home model for the privacy-preserving sensors. These models (average accuracy = 84%) require no in-situ user input. In addition, when VAX is augmented with just one labeled instance for the activities not detected by the VAX A/V pipeline (~2 out of 17), it can detect all 17 activities with an average accuracy of 84%. Our results show that VAX is significantly better than a baseline supervised-learning approach of using one labeled instance per activity in each home (average accuracy of 79%) since VAX reduces the user burden of providing activity labels by 8x (~2 labels vs. 17 labels).
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The increased adoption of smart home cameras (SHCs) foregrounds issues of surveillance, power, and privacy in homes and neighborhoods. However, questions remain about how people are currently using these devices to monitor and surveil, what the benefits and limitations are for users, and what privacy and security tensions arise between primary users and other stakeholders. We present an empirical study with 14 SHC users to understand how these devices are used and integrated within everyday life. Based on semistructured qualitative interviews, we investigate users’ motivations, practices, privacy concerns, and social negotiations. Our findings highlight the SHC as a perceptually powerful and spatially sensitive device that enables a variety of surveillant uses outside of basic home security—from formally surveilling domestic workers, to casually spying on neighbors, to capturing memories. We categorize surveillant SHC uses, clarify distinctions between primary and non primary users, and highlight under-considered design directions for addressing power imbalances among primary and non-primary users.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3491102.3502109
