Ask the Experts: What Should Be on an IoT Privacy and Security Label?
Not on the map. Why?
Abstract (arXiv)
Information about the privacy and security of Internet of Things (IoT) devices is not readily available to consumers who want to consider it before making purchase decisions. While legislators have proposed adding succinct, consumer accessible, labels, they do not provide guidance on the content of these labels. In this paper, we report on the results of a series of interviews and surveys with privacy and security experts, as well as consumers, where we explore and test the design space of the content to include on an IoT privacy and security label. We conduct an expert elicitation study by following a three-round Delphi process with 22 privacy and security experts to identify the factors that experts believed are important for consumers when comparing the privacy and security of IoT devices to inform their purchase decisions. Based on how critical experts believed each factor is in conveying risk to consumers, we distributed these factors across two layers---a primary layer to display on the product package itself or prominently on a website, and a secondary layer available online through a web link or a QR code. We report on the experts' rationale and arguments used to support their choice of factors. Moreover, to study how consumers would perceive the privacy and security information specified by experts, we conducted a series of semi-structured interviews with 15 participants, who had purchased at least one IoT device (smart home device or wearable). Based on the results of our expert elicitation and consumer studies, we propose a prototype privacy and security label to help consumers make more informed IoT-related purchase decisions.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Feb 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 2; 2026: 2.
8 papers.
- 2024Secret Use of Large Language Model (LLM)cited by 9
- 2022Machine Learning Sensorscited by 5
- 2022Leveraging Privacy Profiles to Empower Users in the Digital Societycited by 2
- 2023Datasheets for Machine Learning Sensorscited by 1
- 2025Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scalecited by 1
- 2025An LLM-enabled semantic-centric framework to consume privacy policiescited by 0
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AIcited by 0
- 2026SoK: From Generation to Consumption of Privacy Documents in Software Systemscited by 0
- 2026SoK: From Generation to Consumption of Privacy Documents in Software Systems12 Aug 2026
- 2026Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI19 Apr 2026
- 2025An LLM-enabled semantic-centric framework to consume privacy policies1 Sep 2025
- 2025Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale15 Jul 2025
- 2024Secret Use of Large Language Model (LLM)28 Sep 2024
- 2023Datasheets for Machine Learning Sensors15 Jun 2023
- 2022Machine Learning Sensors7 Jun 2022
- 2022Leveraging Privacy Profiles to Empower Users in the Digital Society1 Apr 2022