Affiliation:
1. National University of Singapore, Singapore
Abstract
Personal data collected from today's wearable sensors contain a rich amount of information that can reveal a user's identity. Differential privacy (DP) is a well-known technique for protecting the privacy of the sensor data being sent to community sensing applications while preserving its statistical properties. However, differential privacy algorithms are computationally expensive, requiring user-level random noise generation which incurs high overheads on wearables with constrained hardware resources. In this paper, we propose SeRaNDiP -- which utilizes the inherent random noise existing in wearable sensors for distributed differential privacy. We show how various hardware configuration parameters available in wearable sensors can enable different amounts of inherent sensor noise and ensure distributed differential privacy guarantee for various community sensing applications with varying sizes of populations. Our evaluations of SeRaNDiP on five wearable sensors that are widely used in today's commercial wearables -- MPU-9250 accelerometer, ADXL345 accelerometer, BMP 388 barometer, MLP 3115A2 barometer, and MLX90632 body temperature sensor show a 1.4X-1.8X computation/communication speedup and 1.2X-1.5X energy savings against state-of-the-art DP implementation. To the best of our knowledge, SeRaNDiP is the first framework to leverage the inherent random sensor noise for differential privacy preservation in community sensing without any hardware modification.
Funder
Singapore National Research Foundation
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction
Reference98 articles.
1. 2015. Chapter 8 -- Noise. In FinFET Modeling for IC Simulation and Design Yogesh Singh Chauhan Darsen D. Lu Sriramkumar Vanugopalan Sourabh Khandelwal Juan Pablo Duarte Navid Paydavosi Ai Niknejad and Chenming Hu (Eds.). Academic Press Oxford 195--202. https://doi.org/10.1016/B978-0-12-420031-9.00008-7 10.1016/B978-0-12-420031-9.00008-7
2015. Chapter 8 -- Noise. In FinFET Modeling for IC Simulation and Design Yogesh Singh Chauhan Darsen D. Lu Sriramkumar Vanugopalan Sourabh Khandelwal Juan Pablo Duarte Navid Paydavosi Ai Niknejad and Chenming Hu (Eds.). Academic Press Oxford 195--202. https://doi.org/10.1016/B978-0-12-420031-9.00008-7
2. 2015. MPU-9250 Register Map and Descriptions Revision 1.6. https://invensense.tdk.com/wp-content/uploads/2015/02/RM-MPU-9250A-00-v1.6.pdf 2015. MPU-9250 Register Map and Descriptions Revision 1.6. https://invensense.tdk.com/wp-content/uploads/2015/02/RM-MPU-9250A-00-v1.6.pdf
3. 2020. Accurate skin ear temperature sensing for wearable applications. https://www.melexis.com/en/documents/documentation/application-flyers/application-flyer-wearables 2020. Accurate skin ear temperature sensing for wearable applications. https://www.melexis.com/en/documents/documentation/application-flyers/application-flyer-wearables
4. 2020. Understanding the Accelerometer Noise Specification. Understanding the accelerometer noise specification (May 2020 ). https://wilcoxon.com/wp-content/uploads/2020/05/Understanding-the-accelerometer-noise-specification_TN33.pdf 2020. Understanding the Accelerometer Noise Specification. Understanding the accelerometer noise specification (May 2020). https://wilcoxon.com/wp-content/uploads/2020/05/Understanding-the-accelerometer-noise-specification_TN33.pdf
5. 2021. AI classifies people's emotions from the way they walk. https://venturebeat.com/2019/07/01/ai-classifies-peoples-emotions-from-the-way-they-walk/ 2021. AI classifies people's emotions from the way they walk. https://venturebeat.com/2019/07/01/ai-classifies-peoples-emotions-from-the-way-they-walk/
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