Problem
Research questions and friction points this paper is trying to address.
Complex Event Processing
Privacy-preserving Mechanisms
Data Utility
Pattern-level Differential Privacy for High-utility Complex Event Processing
🤖 AI Summary
本文提出了一种新的模式级差分隐私方法，通过动态调整添加到数据流中的噪声来提高复杂事件处理系统中检测到的事件模式的实用性。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pattern-level Differential Privacy
Complex Event Processing
Privacy-preserving Mechanisms
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