DeZent: Decentralized z-Anonymity with Privacy-Preserving Coordination

TL;DR

DeZent introduces a decentralized approach to z-anonymity that enhances privacy in sensor networks by reducing reliance on trusted central entities and maintaining efficiency through lightweight coordination.

Contribution

It presents deZent, a novel decentralized implementation of z-anonymity using secure sum and stochastic counting, minimizing trust and communication overhead.

Findings

DeZent achieves similar publication ratios to centralized z-anonymity.

DeZent reduces communication overhead with the central entity.

DeZent maintains privacy while improving system efficiency.

Abstract

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.