模式级差分隐私用于高效复杂事件处理

中文摘要 AI 辅助

当前复杂事件处理（CEP）系统中的隐私保护机制（PPMs）过于严格，降低了数据消费者所接收数据的效用。本文提出了一种在CEP系统中保护隐私的新方法，通过动态调整添加到未受保护数据流中的噪声，提高了所检测事件模式的效用。我们引入了一种新的保证，称为模式级差分隐私（DP），它使我们能够在模式级别应用和比较PPMs的强度。我们提出了新的模式级PPMs，以实现模式级DP，并分析了这些PPMs的不同信任设置及其对CEP系统中上下文知识（例如，部署的查询）的需求。我们的评估基于三个数据集（两个真实世界，一个合成），结果表明，所提出的PPMs在保持与最先进PPMs相同隐私水平的同时，提高了数据效用。我们使用模拟来研究我们所提出的PPMs在各种实际场景中的性能。此外，我们证明了计算复杂性不是部署的障碍。

英文摘要

Current privacy-preserving mechanisms (PPMs) in Complex Event Processing (CEP) systems are unnecessarily restrictive, reducing the utility of data received by data consumers. This article presents a novel approach to preserve privacy in CEP systems, improving the utility of detected event patterns by dynamically adapting the noise added to an unprotected data stream. We introduce a new guarantee named pattern-level differential privacy (DP), which enables us to apply and compare the strength of PPMs at the pattern level. We propose new pattern-level PPMs yielding pattern-level DP and analyze different trust settings of these PPMs and their requirements for context knowledge in the CEP system, e.g., the deployed queries. Our evaluation is based on three datasets (two real-world, one synthetic) and shows that the proposed PPMs increase data utility while preserving the same privacy level as the state-of-the-art PPMs. We use simulations to study the performance of our proposed PPMs in various practical scenarios. Furthermore, we demonstrate that computational complexity is not an obstacle to deployment.

发表机构

• University of Oslo（奥斯陆大学）
• Technische Universität Ilmenau（伊尔梅瑙工业大学）

机构由 AI 辅助整理，请以论文原文为准。