Discovering Smart Home Internet of Things Privacy Norms Using Contextual Integrity
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Abstract (arXiv)
The proliferation of Internet of Things (IoT) devices for consumer "smart" homes raises concerns about user privacy. We present a survey method based on the Contextual Integrity (CI) privacy framework that can quickly and efficiently discover privacy norms at scale. We apply the method to discover privacy norms in the smart home context, surveying 1,731 American adults on Amazon Mechanical Turk. For 2,800 and in less than six hours, we measured the acceptability of 3,840 information flows representing a combinatorial space of smart home devices sending consumer information to first and third-party recipients under various conditions. Our results provide actionable recommendations for IoT device manufacturers, including design best practices and instructions for adopting our method for further research.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 May 2018.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 5; 2026: 4.
11 papers.
- 2025Contextual Agent Security: A Policy for Every Purposecited by 59
- 2025A Survey on Privacy Risks and Protection in Large Language Modelscited by 21
- 2025CASE-Bench: Context-Aware SafEty Benchmark for Large Language Modelscited by 20
- 2024Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metriccited by 6
- 2025Benchmarking LLM Privacy Recognition for Social Robot Decision Makingcited by 4
- 2025Personalizing Agent Privacy Decisions via Logical Entailmentcited by 4
- 2023Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Modelscited by 3
- 2026PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?cited by 2
- 2026From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Managementcited by 0
- 2026PrivacySIM: Evaluating LLM Simulation of User Privacy Behaviorcited by 0
- 2026Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agentscited by 0
- 2026Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents25 Jun 2026
- 2026PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior12 May 2026
- 2026From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management4 Feb 2026
- 2026PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?14 Jan 2026
- 2025Personalizing Agent Privacy Decisions via Logical Entailment4 Dec 2025
- 2025Benchmarking LLM Privacy Recognition for Social Robot Decision Making22 Jul 2025
- 2025A Survey on Privacy Risks and Protection in Large Language Models4 May 2025
- 2025Contextual Agent Security: A Policy for Every Purpose28 Jan 2025
- 2025CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models24 Jan 2025
- 2024Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric5 Sep 2024
- 2023Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models3 Nov 2023