Privacy-Aware Sharing of Raw Spatial Sensor Data for Cooperative Perception
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911331258466304 |
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| author | Liu, Bangya Yan, Chengpo Jiang, Chenghao Banerjee, Suman Prabhakara, Akarsh |
| author_facet | Liu, Bangya Yan, Chengpo Jiang, Chenghao Banerjee, Suman Prabhakara, Akarsh |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_16265 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Privacy-Aware Sharing of Raw Spatial Sensor Data for Cooperative Perception Liu, Bangya Yan, Chengpo Jiang, Chenghao Banerjee, Suman Prabhakara, Akarsh Networking and Internet Architecture Computer Vision and Pattern Recognition Robotics 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. |
| title | Privacy-Aware Sharing of Raw Spatial Sensor Data for Cooperative Perception |
| topic | Networking and Internet Architecture Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2512.16265 |