事件流的隐私保护过滤
Privacy-Preserving Filtering for Event Streams
AI总结：
本文提出一种在多输入多输出系统中实现差分隐私保护的方法，通过优化零强迫均衡机制的扩展形式，用于实时发布用户活动统计数据，以实现对建筑内occupancy的隐私保护监控与预测。
摘要：
许多大规模信息系统，如智能交通系统、智能电网或智能建筑，收集用户活动数据以优化运营。为鼓励参与和采用这些系统，设计过程必须考虑隐私问题。在典型场景中，信号来自多个传感器，捕捉涉及用户的事件，并需要持续实时发布感兴趣的统计信息。本文考虑了为处理事件流的多输入多输出系统提供差分隐私保证的问题。我们展示了如何构建和优化各种零强迫均衡机制的扩展形式，该机制此前用于单输入单输出系统。其中一些扩展可以考虑输入信号的模型。我们通过私密监控和预测配备多运动检测传感器的建筑内occupancy的问题，展示了隐私保护过滤设计方法。
英文摘要：
Many large-scale information systems such as intelligent transportation systems, smart grids or smart buildings collect data about the activities of their users to optimize their operations. To encourage participation and adoption of these systems, it is becoming increasingly important that the design process take privacy issues into consideration. In a typical scenario, signals originate from many sensors capturing events involving the users, and several statistics of interest need to be continuously published in real-time. This paper considers the problem of providing differential privacy guarantees for such multi-input multi-output systems processing event streams. We show how to construct and optimize various extensions of the zero-forcing equalization mechanism, which we previously proposed for single-input single-output systems. Some of these extensions can take a model of the input signals into account. We illustrate our privacy-preserving filter design methodology through the problem of privately monitoring and forecasting occupancy in a building equipped with multiple motion detection sensors.
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。
