Privacy-by-Sensing with Time-domain Differentially-Private Compressed Sensing
Jianbo Liu, Boyang Cheng, Pengyu Zeng, Steven Davis, Muya Chang, Ningyuan Cao · 2023
With the ubiquitous IoT sensors and enormous real-time data generation, data privacy is becoming a critical societal concern. State-of-the-art privacy protection methods all demand significant hardware overhead due to computation-insensitive algorithms and divided sensor/security architecture. In this paper, we propose a generic time-domain circuit architecture that protects raw data by enabling a differentially-private compressed sensing (DP-CS) algorithm secured by physical unclonable functions (PUF). To address privacy concerns and hardware overhead at the same time, a robust unified PUF and time-domain mixed-signal (TD-MS) module are designed, where PUF enables private and secure entropy generation. To evaluate the proposed design against a digital baseline, we performed experiments based on synthesized circuits and SPICE simulation and measured a 2.9x area reduction and 3.2x energy gains. We also measured high-quality PUF generation with TD-MS circuit with a inter-die Hamming distance of 52% and a low intra-die Hamming distance of 2.8%. Furthermore, we performed attack and algorithm performance measurements demonstrating the proposed design preserves data privacy even under attack, and the machine learning performance has minimal degradation (within 2%) compared to the digital baseline.