Enhancing User Experience with Visual Controls for Local Differential Privacy

While Local Differential Privacy (LDP) offers strong privacy guarantees for IoT data collection, users often struggle to understand its implications and control their privacy settings. This paper presents a user-centric approach to implementing LDP in smart home environments, focusing on voice command privacy. We analyze privacy control patterns across major smart home platforms and propose a novel interface that translates complex LDP parameters into four intuitive privacy levels. The interface combines visual controls with concrete examples showing how privacy transformations affect voice commands. By mapping mathematical privacy parameters to user-friendly settings while maintaining theoretical guarantees, our approach explores making differential privacy more accessible in IoT environments. We validated our design through a usability study to understand its strengths in accessibility and key areas for refinement.

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Publication Details

Journal
Journal of Cybersecurity and Privacy
Published
2025-06-22
DOI
https://doi.org/10.3390/jcp5030036
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
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article

Enhancing User Experience with Visual Controls for Local Differential Privacy

Xueting Li, Amin Milani Fard, Song Dong
Journal of Cybersecurity and Privacy
Privacy-Preserving Technologies in Data
article

Enhancing User Experience with Visual Controls for Local Differential Privacy

Xueting Li, Amin Milani Fard, Song Dong
article en

Abstract

While Local Differential Privacy (LDP) offers strong privacy guarantees for IoT data collection, users often struggle to understand its implications and control their privacy settings. This paper presents a user-centric approach to implementing LDP in smart home environments, focusing on voice command privacy. We analyze privacy control patterns across major smart home platforms and propose a novel interface that translates complex LDP parameters into four intuitive privacy levels. The interface combines visual controls with concrete examples showing how privacy transformations affect voice commands. By mapping mathematical privacy parameters to user-friendly settings while maintaining theoretical guarantees, our approach explores making differential privacy more accessible in IoT environments. We validated our design through a usability study to understand its strengths in accessibility and key areas for refinement.

Journal of Cybersecurity and PrivacyVol. 5(3)
New York Institute of Technology (CA)
Openalex Percentile: Top 93%
Privacy-Preserving Technologies in Data
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Enhancing User Experience with Visual Controls for Local Differential Privacy — Xueting Li, Amin Milani Fard, et al. · Journal of Cybersecurity and Privacy (2025) | TGRS Research Map | TGRS