Interdependent Privacy in Smart Homes: Hunting for Bystanders in Privacy Policies

• URL: http://arxiv.org/abs/2510.26523v1
• Date: Thu, 30 Oct 2025 14:16:21 GMT
• Title: Interdependent Privacy in Smart Homes: Hunting for Bystanders in Privacy Policies
• Authors: Shuaishuai Liu, Gergely Acs, Gergely Biczók,
• Abstract summary: This paper presents a focused privacy policy analysis of 20 video doorbell and smart camera products.<n>We show that although some of the vendors acknowledge bystanders, they address it only to the extent of including disclaimers.<n>We identify and examine real-world cases related to bystander privacy, demonstrating how current deployments can impact non-users.
• Score: 0.8602553195689513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Smart home devices such as video doorbells and security cameras are becoming increasingly common in everyday life. While these devices offer convenience and safety, they also raise new privacy concerns: how these devices affect others, like neighbors, visitors, or people passing by. This issue is generally known as interdependent privacy, where one person's actions (or inaction) may impact the privacy of others, and, specifically, bystander privacy in the context of smart homes. Given lax data protection regulations in terms of shared physical spaces and amateur joint data controllers, we expect that the privacy policies of smart home products reflect the missing regulatory incentives. This paper presents a focused privacy policy analysis of 20 video doorbell and smart camera products, concentrating explicitly on the bystander aspect. We show that although some of the vendors acknowledge bystanders, they address it only to the extent of including disclaimers, shifting the ethical responsibility for collecting the data of non-users to the device owner. In addition, we identify and examine real-world cases related to bystander privacy, demonstrating how current deployments can impact non-users. Based on our findings, we analyze vendor privacy policies in light of existing legal frameworks and technical capabilities, and we provide practical recommendations for both policy language and system design to enhance transparency and empower both bystanders and device owners.

Related papers

• Privacy Bills of Materials: A Transparent Privacy Information Inventory for Collaborative Privacy Notice Generation in Mobile App Development [23.41168782020005]
  We introduce PriBOM, a systematic software engineering approach to better capture and coordinate mobile app privacy information.<n>PriBOM facilitates transparency-centric privacy documentation and specific privacy notice creation, enabling traceability and trackability of privacy practices.
  arXiv  Detail & Related papers  (2025-01-02T08:14:52Z)
• Activity Recognition on Avatar-Anonymized Datasets with Masked Differential Privacy [64.32494202656801]
  Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence.<n>We present anonymization pipeline that replaces sensitive human subjects in video datasets with synthetic avatars within context.<n>We also proposeMaskDP to protect non-anonymized but privacy sensitive background information.
  arXiv  Detail & Related papers  (2024-10-22T15:22:53Z)
• PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action [54.11479432110771]
  PrivacyLens is a novel framework designed to extend privacy-sensitive seeds into expressive vignettes and further into agent trajectories.<n>We instantiate PrivacyLens with a collection of privacy norms grounded in privacy literature and crowdsourced seeds.<n>State-of-the-art LMs, like GPT-4 and Llama-3-70B, leak sensitive information in 25.68% and 38.69% of cases, even when prompted with privacy-enhancing instructions.
  arXiv  Detail & Related papers  (2024-08-29T17:58:38Z)
• Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory [43.12744258781724]
  We formulate the privacy issue as a reasoning problem rather than simple pattern matching.<n>We develop the first comprehensive checklist that covers social identities, private attributes, and existing privacy regulations.
  arXiv  Detail & Related papers  (2024-08-19T14:48:04Z)
• PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration [20.05248442344211]
  PrivacyRestore is a plug-and-play method to protect the privacy of user inputs during inference.<n>We create three datasets, covering medical and legal domains, to evaluate the effectiveness of PrivacyRestore.
  arXiv  Detail & Related papers  (2024-06-03T14:57:39Z)
• A Study of the Landscape of Privacy Policies of Smart Devices [1.2642388972233845]
  This paper presents a comprehensive study of the landscape of privacy policies of smart devices. We introduce a methodology that addresses the unique challenges of smart devices, by finding information about them, their manufacturers, and their privacy policies on the Web. Overall, we analyzed 4,556 smart devices, 2,211 manufacturers, and 819 privacy policies.
  arXiv  Detail & Related papers  (2023-08-11T00:52:31Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• Privacy Explanations - A Means to End-User Trust [64.7066037969487]
  We looked into how explainability might help to tackle this problem. We created privacy explanations that aim to help to clarify to end users why and for what purposes specific data is required. Our findings reveal that privacy explanations can be an important step towards increasing trust in software systems.
  arXiv  Detail & Related papers  (2022-10-18T09:30:37Z)
• PrivHAR: Recognizing Human Actions From Privacy-preserving Lens [58.23806385216332]
  We propose an optimizing framework to provide robust visual privacy protection along the human action recognition pipeline. Our framework parameterizes the camera lens to successfully degrade the quality of the videos to inhibit privacy attributes and protect against adversarial attacks.
  arXiv  Detail & Related papers  (2022-06-08T13:43:29Z)
• The Evolving Path of "the Right to Be Left Alone" - When Privacy Meets Technology [0.0]
  This paper proposes a novel vision of the privacy ecosystem, introducing privacy dimensions, the related users' expectations, the privacy violations, and the changing factors. We believe that promising approaches to tackle the privacy challenges move in two directions: (i) identification of effective privacy metrics; and (ii) adoption of formal tools to design privacy-compliant applications.
  arXiv  Detail & Related papers  (2021-11-24T11:27:55Z)
• The Challenges and Impact of Privacy Policy Comprehension [0.0]
  This paper experimentally manipulated the privacy-friendliness of an unavoidable and simple privacy policy. Half of our participants miscomprehended even this transparent privacy policy. To mitigate such pitfalls we present design recommendations to improve the quality of informed consent.
  arXiv  Detail & Related papers  (2020-05-18T14:16:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.