Entropy Collapse in Mobile Sensors: The Hidden Risks of Sensor-Based Security

• URL: http://arxiv.org/abs/2502.09535v7
• Date: Wed, 23 Apr 2025 16:40:52 GMT
• Title: Entropy Collapse in Mobile Sensors: The Hidden Risks of Sensor-Based Security
• Authors: Carlton Shepherd, Elliot Hurley,
• Abstract summary: We systematically analyse the entropy of mobile sensor data across four diverse datasets spanning multiple contexts.<n>Our results demonstrate that adversaries may feasibly predict sensor signals through an exhaustive, brute-force exploration of the entire measurement space.
• Score: 1.6267479602370545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mobile sensor data has been proposed for security-critical applications such as device pairing, proximity detection, and continuous authentication. However, the foundational premise that these signals provide sufficient entropy remains under-explored. In this work, we systematically analyse the entropy of mobile sensor data across four diverse datasets spanning multiple contexts. Our findings reveal pervasive biases, with single-sensor mean min-entropy values ranging from 3.408-4.483 bits (S.D.=1.018-1.574) despite Shannon entropy being several multiples higher, showing a significant collapse between average- to worst-case settings. We further demonstrate that correlations between sensor modalities reduce the worst-case entropy of using multiple sensors by up to ~75% compared to average-case Shannon entropy. This brings joint min-entropy well below 10 bits in many cases and, in the best case, yielding only ~24 bits of min-entropy when combining 20 sensor modalities. Our results demonstrate that adversaries may feasibly predict sensor signals through an exhaustive, brute-force exploration of the entire measurement space. Our work also calls into question the widely held assumption that adding more sensors inherently yields higher security, and we strongly urge caution when relying on mobile sensor data for security applications.

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