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）是一种强大且日益重要的工具，用于物联网（IoT）应用中的数据流分析。这些数据流通常包含需要适当保护的隐私信息。然而，CEP系统中的隐私保护仍处于起步阶段，大多数现有的隐私保护机制（PPM）直接沿用为数据流设计的方案。此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。本文尝试突破这一限制，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。我们引入两种保证模式级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，从而提升数据质量。其中一种PPM提供自适应隐私保护，具有更高粒度和泛化能力。我们通过在真实数据集和合成数据集上的两项实验评估了所提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，在同等强度的隐私保障下，本文提出的隐私保障及其PPM能够提供更优的数据质量。