Analyzing large volumes of sensor network data, such as electricity consumption measurements from smart meters, is essential for modern applications but raises significant privacy concerns. Privacy-enhancing technologies like z-anonymity offer efficient anonymization for continuous data streams by suppressing rare values that could lead to re-identification, making it particularly suited for resource-constrained environments. Originally designed for centralized architectures, z-anonymity assumes a trusted central entity. In this paper, we introduce deZent, a decentralized implementation of z-anonymity that minimizes trust in the central entity by realizing local z-anonymity with lightweight coordination. We develop deZent using a stochastic counting structure and secure sum to coordinate private anonymization across the network. Our results show that deZent achieves comparable performance to centralized z-anonymity in terms of publication ratio, while reducing the communication overhead towards the central entity. Thus, deZent presents a promising approach for enhancing privacy in sensor networks while preserving system efficiency.

翻译：分析传感器网络产生的大规模数据（例如智能电表的电力消耗测量数据）对现代应用至关重要，但也引发了显著的隐私担忧。隐私增强技术如z-匿名化通过对可能导致重新识别的罕见值进行抑制，为连续数据流提供了高效的匿名化处理，使其特别适用于资源受限的环境。z-匿名化最初为集中式架构设计，其假设存在一个可信的中心实体。本文提出deZent，一种去中心化的z-匿名化实现方案，它通过轻量级协调实现本地z-匿名化，从而最小化对中心实体的信任依赖。我们利用随机计数结构和安全求和协议构建deZent，以协调整个网络的私有匿名化过程。实验结果表明，deZent在发布率方面达到了与集中式z-匿名化相当的性能，同时显著降低了与中心实体的通信开销。因此，deZent为在保护系统效率的同时增强传感器网络隐私提供了一种具有前景的解决方案。