# Discovering Smart Home Internet of Things Privacy Norms Using Contextual Integrity

arXiv: https://arxiv.org/abs/1805.06031 · HTML: https://mlsanity.com/paper/1805.06031/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2018.

## Built on by (most cited first, 11 of 11)

- [Contextual Agent Security: A Policy for Every Purpose](https://mlsanity.com/paper/2501.17070/) (2025)
- [A Survey on Privacy Risks and Protection in Large Language Models](https://mlsanity.com/paper/2505.01976/) (2025)
- [CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models](https://mlsanity.com/paper/2501.14940/) (2025)
- [Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric](https://mlsanity.com/paper/2409.03735/) (2024)
- [Benchmarking LLM Privacy Recognition for Social Robot Decision Making](https://mlsanity.com/paper/2507.16124/) (2025)
- [Personalizing Agent Privacy Decisions via Logical Entailment](https://mlsanity.com/paper/2512.05065/) (2025)
- [Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models](https://mlsanity.com/paper/2311.02192/) (2023)
- [PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?](https://mlsanity.com/paper/2601.09152/) (2026)
- [From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management](https://mlsanity.com/paper/2602.05016/) (2026)
- [PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior](https://mlsanity.com/paper/2605.12147/) (2026)
- [Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents](https://mlsanity.com/paper/2606.26627/) (2026)

## 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.