Paper Type
ERF
Abstract
With the rapid expansion of the IoT, concerns around user privacy have intensified, particularly in the context of smart home devices where personal routines, behaviors, and biometric data are deeply embedded in daily life. Many top-selling smart home devices lack adequate privacy policies and rely on complex, legalistic text that users struggle to understand. Drawing on privacy calculus theory, this study addresses the resulting information asymmetry to examine how users weigh convenience against risk when adopting IoT devices. Using a design science approach guided by the DSRM framework, we present FairPrivacy: a robust Q&A system that leverages LLMs to handle dialect-diverse queries, provide plain-language policy summaries, and highlight known security vulnerabilities. Preliminary analysis reveals widespread transparency gaps across IoT vendors, reinforcing the need for accessible, dialect-resistant tools. Through user studies, we aim to show how FairPrivacy empowers consumers to make more informed, privacy-conscious choices in the smart home market.
Paper Number
1814
Recommended Citation
Richardson, Turquoise H.; Klisura, Đorđe; Pataci, Hilal; and Rios, Anthony, "FairPrivacy: A Robust Q&A System for Enhancing Transparency in IoT" (2025). AMCIS 2025 Proceedings. 48.
https://aisel.aisnet.org/amcis2025/sig_sec/sig_sec/48
FairPrivacy: A Robust Q&A System for Enhancing Transparency in IoT
With the rapid expansion of the IoT, concerns around user privacy have intensified, particularly in the context of smart home devices where personal routines, behaviors, and biometric data are deeply embedded in daily life. Many top-selling smart home devices lack adequate privacy policies and rely on complex, legalistic text that users struggle to understand. Drawing on privacy calculus theory, this study addresses the resulting information asymmetry to examine how users weigh convenience against risk when adopting IoT devices. Using a design science approach guided by the DSRM framework, we present FairPrivacy: a robust Q&A system that leverages LLMs to handle dialect-diverse queries, provide plain-language policy summaries, and highlight known security vulnerabilities. Preliminary analysis reveals widespread transparency gaps across IoT vendors, reinforcing the need for accessible, dialect-resistant tools. Through user studies, we aim to show how FairPrivacy empowers consumers to make more informed, privacy-conscious choices in the smart home market.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
SIGSEC