Balancing The Perception of Cheating Detection, Privacy and Fairness: A Mixed-Methods Study of Visual Data Obfuscation in Remote Proctoring
Suvadeep Mukherjee, Verena Distler, Gabriele Lenzini, Pedro, Cardoso-Leite

TL;DR
This study investigates how region-specific visual obfuscation in remote proctoring can balance cheating detection, privacy, and fairness, revealing that advanced methods improve perceptions but may reduce perceived detection effectiveness.
Contribution
It introduces a mixed-methods approach combining expert insights and user perceptions to optimize obfuscation techniques in remote proctoring systems.
Findings
Advanced obfuscation improves privacy and fairness perceptions.
Conventional blurring is preferred for willingness to share videos.
Perceived detection effectiveness decreases with advanced obfuscation.
Abstract
Remote proctoring technology, a cheating-preventive measure, often raises privacy and fairness concerns that may affect test-takers' experiences and the validity of test results. Our study explores how selectively obfuscating information in video recordings can protect test-takers' privacy while ensuring effective and fair cheating detection. Interviews with experts (N=9) identified four key video regions indicative of potential cheating behaviors: the test-taker's face, body, background and the presence of individuals in the background. Experts recommended specific obfuscation methods for each region based on privacy significance and cheating behavior frequency, ranging from conventional blurring to advanced methods like replacement with deepfake, 3D avatars and silhouetting. We then conducted a vignette experiment with potential test-takers (N=259, non-experts) to evaluate their…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Ethics and Social Impacts of AI · Privacy, Security, and Data Protection
