Privacy-Aware Sharing of Raw Spatial Sensor Data for Cooperative Perception

TL;DR

This paper discusses privacy challenges in sharing raw spatial sensor data for vehicle cooperative perception and proposes the SHARP framework to mitigate privacy risks and promote data sharing.

Contribution

It introduces the SHARP framework to reduce privacy leakage in raw sensor data sharing for cooperative perception.

Findings

Identifies privacy concerns as barriers to data sharing

Proposes the SHARP framework for privacy preservation

Discusses open research questions for implementation

Abstract

Cooperative perception between vehicles is poised to offer robust and reliable scene understanding. Recently, we are witnessing experimental systems research building testbeds that share raw spatial sensor data for cooperative perception. While there has been a marked improvement in accuracies and is the natural way forward, we take a moment to consider the problems with such an approach for eventual adoption by automakers. In this paper, we first argue that new forms of privacy concerns arise and discourage stakeholders to share raw sensor data. Next, we present SHARP, a research framework to minimize privacy leakage and drive stakeholders towards the ambitious goal of raw data based cooperative perception. Finally, we discuss open questions for networked systems, mobile computing, perception researchers, industry and government in realizing our proposed framework.