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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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PrivacyOracle: Configuring Sensor Privacy Firewalls with Large Language Models in Smart Built Environments
Modern smart buildings and environments rely on sensory infrastructure to capture and process information about their inhabitants. However, it remains challenging to ensure that this infrastructure complies with privacy norms, preferences, and regulations; individuals occupying smart environments are often occupied with their tasks, lack awareness of the surrounding sensing mechanisms, and are non-technical experts. This problem is only exacerbated by the increasing number of sensors being deployed in these environments, as well as services seeking to use their sensory data. As a result, individuals face an unmanageable number of privacy decisions, preventing them from effectively behaving as their own “privacy firewall” for filtering and managing the multitude of personal information flows. These decisions often require qualitative reasoning over privacy regulations, understanding privacy-sensitive contexts, and applying various privacy transformations when necessary We propose the use of Large Language Models (LLMs), which have demonstrated qualitative reasoning over social/legal norms, sensory data, and program synthesis, all of which are necessary for privacy firewalls. We present PrivacyOracle, a prototype system for configuring privacy firewalls on behalf of users using LLMs, enabling automated privacy decisions in smart built environments. Our evaluation shows that PrivacyOracle achieves up to
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- Award ID(s):
- 1705135
- PAR ID:
- 10540443
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2770-8411
- ISBN:
- 979-8-3503-5487-4
- Page Range / eLocation ID:
- 239 to 245
- Format(s):
- Medium: X
- Location:
- San Francisco, CA, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SPW63631.2024.00028
