SenseHash: Computing on Sensor Values Mystified at the Origin

Nojan Sheybani, Xinqiao Zhang, Siam Umar Hussain, Farinaz Koushanfar

Abstract: We propose SenseHash, a novel design for the lightweight in-hardware mystification of the sensed data at the origin. The framework aims to ensure the privacy of sensitive sensor values while preserving their utility. The sensors are assumed to interface to various (potentially malicious) communication and computing components in the Internet-of-things (IoT) and other emerging pervasive computing scenarios. The primary security primitives of our work are Locality Sensitive Hashing (LSH) combined with Differential Privacy (DP) and secure construction of LSH. Our construction allows (i) sub-linear search in sensor readings while ensuring their security against triangulation attack, and (ii) differentially private statistics of the readings. SenseHash includes hardware architecture as well as accompanying protocols to efficiently utilize the secure readings in practical scenarios. Alongside these scenarios, we present an automated workflow to generalize the application of the mystified readings. Proof-of-concept FPGA implementation of the system demonstrates its practicability and low overhead in terms of hardware resources, energy consumption, and protocol execution time.