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Demonstration of Indoor Location Privacy Enforcement using Obfuscation ?

机译:使用混淆演示室内位置隐私执法

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We apply discrete-event-control-theoretic techniques for opacity enforcement by insertion or deletion of output events to the problem of location privacy enforcement in an indoor environment where users are continuously monitored by IoT devices. We design an obfuscator of user trajectories in a grid model with obstacles. The obfuscator must preserve a secret (e.g., visits to secret cells of the grid), while at the same time enforce feasibility and utility constraints for obfuscated trajectories. We implement the obfuscator to map the true location of the user to an obfuscated location, in real time, using services provided by a data server called the Global Data Plane which records sensor readings from IoT devices and publishes them to subscribers. We explain how scalability of obfuscator synthesis (off-line) and instantiation (online) is achieved. We demonstrate the approach on a grid with over 1,500 cells modeling the first floor of a university building, where location estimation is achieved using the ALPS Acoustic Location Processing System.
机译:我们将离散事件控制理论技术用于通过插入或删除输出事件来增强不透明度,以解决室内环境中位置隐私保护的问题,在该环境中,用户会受到IoT设备的持续监控。我们在带有障碍的网格模型中设计了用户轨迹的混淆器。混淆器必须保留一个秘密(例如,访问网格的秘密单元),同时要对混淆的轨迹实施可行性和实用性约束。我们使用名为Global Data Plane的数据服务器提供的服务来实现混淆器,以将用户的真实位置实时映射到混淆的位置,该服务器记录来自IoT设备的传感器读数并将其发布给订户。我们解释了如何实现混淆器合成(离线)和实例化(在线)的可伸缩性。我们在一个有1,500多个单元的网格上演示了该方法,该网格模拟了大学建筑的一楼,其中使用ALPS声学位置处理系统实现了位置估计。

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