A Privacy-Preserving Localization Scheme with Node Selection in Mobile Networks

TL;DR

The paper tackles location privacy in crowdsourced ToA localization by protecting both targets and anchors while preserving high positioning accuracy. It introduces PPLZN, a privacy-preserving scheme that combines zero-sum noise generation based on Paillier homomorphic encryption with a node-selection mechanism driven by GDOP contributions to reduce overhead in dense deployments. The approach provides formal privacy guarantees under an honest-but-curious model and demonstrates substantial reductions in computation and communication costs, with competitive localization accuracy compared to state-of-the-art cryptographic schemes. The framework is validated through simulations in large 3D fields and shows practical applicability for resource-constrained networks such as UAV swarms.

Abstract

Localization in mobile networks has been widely applied in many scenarios. However, an entity responsible for location estimation exposes both the target and anchors to potential location leakage at any time, creating serious security risks. Although existing studies have proposed privacy-preserving localization algorithms, they still face challenges of insufficient positioning accuracy and excessive communication overhead. In this article, we propose a privacy-preserving localization scheme, named PPLZN. PPLZN protects protects the location privacy of both the target and anchor nodes in crowdsourced localization. Simulation results validate the effectiveness of PPLZN. Evidently, it can achieve accurate position estimation without location leakage and outperform state-of-the-art approaches in both positioning accuracy and communication overhead. In addition, PPLZN significantly reduces computational and communication overhead in large-scale deployments, making it well-fitted for practical privacy-preserving localization in resource-constrained networks.

Figures (10)

• Figure 1: Overview of cooperative positioning scenario. The target estimates distances to four surrounding anchors (${d}_{1}$-${d}_{4}$) and utilizes a multilateration approach to acquire its own position $\mathbf{p}_{0}$ by leveraging location-related information from nearby mobile anchors.
• Figure 2: Algorithm framework of PPLZN. The framework comprises three modules: zero-sum noise generation module (left) to protect privacy, ToA-based privacy-preserving localization module (middle) to estimate the target's position, and node selection algorithm module (right) to improve computation efficiency. The zero-sum noise generation module encrypts location-related information, then the localization module decrypts the ciphertext and provides estimated positions to the node selection algorithm module, and finally the optimal anchors are obtained for the next iteration.
• Figure 3: Zero-sum noise generation based on Paillier encryption.
• Figure 4: Equation decomposition and encryption method. $\mathbf x$ is decomposed into two matrices based on (7) and further transformed into three summation expressions, each protected with a suitable encryption method.
• Figure 5: Flowchart to the proposed privacy-preserving localization algorithm, where anchor 1 is removed from the node selection algorithm.

Theorems & Definitions (4)

• Proposition 1
• proof
• Proposition 2
• proof