Preserving privacy in mobile spatial computing
Preserving privacy in mobile spatial computing
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DOI：
10.1145/3534088.3534343
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
Nan Wu;Ruizhi Cheng;Songqing Chen;Bo Han
中科院分区：
文献类型：
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作者：
Nan Wu;Ruizhi Cheng;Songqing Chen;Bo Han
映射和定位是移动空间计算中的关键组成部分，用于促进用户与物理世界数字模型之间的交互。为了实现定位，移动设备不断捕捉现实世界周围环境的图像，并将其上传到带有空间地图的服务器以进行定位。这导致了对空间地图和定位图像中敏感信息可能泄露的隐私担忧（例如，在机密工业环境或我们的家中使用时）。受上述问题的启发，我们在本文中提出了一个全面的研究议程，用于设计在空间映射和定位中保护隐私的原则性方法。我们介绍了我们正在进行的研究，包括学习辅助噪声生成以保护空间地图、具有智能聚合的分布式架构以保护定位图像，以及使用全同态加密的端到端隐私保护。我们还讨论了这些领域中的技术挑战、我们的初步结果以及开放的研究问题。
Mapping and localization are the key components in mobile spatial computing to facilitate interactions between users and the digital model of the physical world. To enable localization, mobile devices keep capturing images of the real-world surroundings and uploading them to a server with spatial maps for localization. This leads to privacy concerns on the potential leakage of sensitive information in both spatial maps and localization images (e.g., when used in confidential industrial settings or our homes). Motivated by the above issues, we present a holistic research agenda in this paper for designing principled approaches to preserve privacy in spatial mapping and localization. We introduce our ongoing research, including learning-assisted noise generation to shield spatial maps, distributed architecture with intelligent aggregation to protect localization images, and end-to-end privacy preservation with fully homomorphic encryption. We also discuss the technical challenges, our preliminary results, and open research problems in those areas.