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This paper introduces deZent, a decentralized implementation of z-anonymity for privacy-preserving data stream anonymization in sensor networks. DeZent achieves local z-anonymity through a stochastic counting structure and secure sum, minimizing trust in a central entity. Experiments demonstrate that deZent attains comparable publication ratios to centralized z-anonymity while reducing communication overhead to the central entity.
Decentralized z-anonymity is now practical: deZent achieves comparable performance to centralized approaches while minimizing reliance on a trusted central entity.
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.
