SeRaNDiP: Leveraging Inherent Sensor Random Noise for Differential Privacy Preservation in Wearable Community Sensing Applications

Ayanga Kalupahana, Ananta Narayanan Balaji, Xiaokui Xiao and Li-Shiuan Peh
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.