提出SHARP框架，保护车辆原始传感数据共享中的隐私
Privacy-Aware Sharing of Raw Spatial Sensor Data for Cooperative Perception
- 设计隐私保护框架SHARP，减少原始数据共享时的隐私泄露
- 支持车企在保障隐私前提下实现高精度协同感知
- 适合关注车联网隐私与数据安全的研究者和行业开发者
车辆间的协同感知有望实现鲁棒可靠的环境理解。近年来，实验系统研究正构建测试平台，通过共享原始空间传感器数据来提升感知性能。尽管准确率显著提高，且是未来方向，但该方法在实际推广中面临新形式的隐私担忧。本文首先指出共享原始数据会引发新的隐私问题，阻碍车企采纳；随后提出SHARP框架，旨在最小化隐私泄露，推动实现基于原始数据的协同感知目标；最后讨论了网络系统、移动计算、感知研究、产业界及政府需共同解决的开放问题。
原文摘要 · Abstract (English)
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.
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