Presentation
FAxC: Exploiting Feature Approximation for Privacy Preservation in Human Activity Recognition
DescriptionReal-time health-monitoring systems are generating significantly large amounts of data, challenging memory storage space, computation capacity of processing units, and power budget for transmission. This work proposes a new approximation method, FAxC, which exploits the features in biometric data to perform multi-dimensional approximation. The proposed feature-oriented approximation not only significantly reduces the size of sensing data without compromising accuracy but also addresses privacy preservation issues in healthcare applications. The FAxC method has been successfully deployed to a machine-learning(ML)-based human activity recognition (HAR) framework. The experimental results based on a public database, MotionSense, show that FAxC can achieve a HAR accuracy of over 94.85% with a 34% reduction in data size. More importantly, this work provides quantitative assessments of the trade-off between HAR accuracy, privacy-preserving rate, and approximation efficiency. Regarding privacy preservation, our case study indicates that FAxC outperforms the existing downsampling and single-feature approximation methods by up to 3.2x and 2.8x, respectively. A tinyML-based FPGA implementation for the HAR application shows that the use of FAxC reduces the classification latency by 36% compared to a non-approximation baseline.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby