CollabLoc：基于协同信息融合的隐私保护多模定位
CollabLoc: Privacy-Preserving Multi-Modal Localization via Collaborative Information Fusion
摘要：
移动电话为构建上下文感知应用程序提供了一个绝佳的机会。特别地，基于位置的服务是重要的上下文感知服务，其越来越多地用于实施安全策略、用于支持室内房间导航以及用于提供个性化帮助。然而，一个主要的问题仍然没有得到解决--缺乏跨建筑物工作的解决方案，同时不使用额外的基础设施，也考虑到隐私和可靠性的需求。本文提出了一种基于Wi-Fi RSSI（现有基础设施）、蜂窝RSSI、声音和光线水平的隐私保护、多模态、跨建筑、协作定位平台，其使房间级定位成为主要应用（尽管子房间级粒度是可能的）。隐私本质上内置于基于洋葱路由和扰动/随机化技术的解决方案中，并利用加权协作的思想来增加可靠性以及限制嘈杂设备的影响（由于传感器噪声/隐私）。已分析了所提出的解决方案的隐私，准确性，最佳参数，以及其他开销的位置数据收集在多个室内和室外位置使用Android应用程序。
英文摘要：
Mobile phones provide an excellent opportunity for building context-aware applications. In particular, location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed---the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. In this paper, a privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular RSSI, sound and light levels, that enables room-level localization as main application (though sub room level granularity is possible). The privacy is inherently built into the solution based on onion routing, and perturbation/randomization techniques, and exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The proposed solution has been analyzed in terms of privacy, accuracy, optimum parameters, and other overheads on location data collected at multiple indoor and outdoor locations using an Android app.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
