Complex event processing (CEP) is a powerful and increasingly more important tool to analyse data streams for Internet of Things (IoT) applications. These data streams often contain private information that requires proper protection. However, privacy protection in CEP systems is still in its infancy, and most existing privacy-preserving mechanisms (PPMs) are adopted from those designed for data streams. Such approaches undermine the quality of the entire data stream and limit the performance of IoT applications. In this paper, we attempt to break the limitation and establish a new foundation for PPMs of CEP by proposing a novel pattern-level differential privacy (DP) guarantee. We introduce two PPMs that guarantee pattern-level DP. They operate only on data that correlate with private patterns rather than on the entire data stream, leading to higher data quality. One of the PPMs provides adaptive privacy protection and brings more granularity and generalization. We evaluate the performance of the proposed PPMs with two experiments on a real-world dataset and on a synthetic dataset. The results of the experiments indicate that our proposed privacy guarantee and its PPMs can deliver better data quality under equally strong privacy guarantees, compared to multiple well-known PPMs designed for data streams.

翻译：复杂事件处理（CEP）是一种强大且在物联网（IoT）应用中日益重要的数据流分析工具。这些数据流通常包含需要适当保护的私有信息。然而，CEP系统中的隐私保护仍处于起步阶段，现有的大多数隐私保护机制（PPM）是从为数据流设计的机制中借鉴而来。这类方法会损害整个数据流的质量，并限制物联网应用的性能。本文试图突破这一局限，通过提出一种新颖的模式级差分隐私（DP）保障，为CEP的PPM建立新基础。我们引入了两种保障模式级DP的PPM。它们仅对与私有模式相关的数据进行操作，而非作用于整个数据流，从而提升了数据质量。其中一种PPM提供自适应隐私保护，实现了更细粒度和更泛化的效果。我们通过在真实数据集和合成数据集上进行的两项实验，评估了所提出PPM的性能。实验结果表明，与多种为数据流设计的知名PPM相比，我们的隐私保障及其PPM在同等强度的隐私保护下，能够提供更高的数据质量。