A Local Differential Privacy Hybrid Data Clustering Iterative Algorithm for Edge Computing

Abstract

As a new computing method, edge computing not only improves the computing efficiency and processing power of data, but also reduces the transmission delay of data. Due to the wide variety of edge devices and the increasing amount of terminal data, third-party data centers are unable to ensure no user privacy data leaked. To solve these problems, this paper proposes an iterative clustering algorithm named local differential privacy iterative aggregation (LDPIA) based on localized differential privacy, which implements local differential privacy. To address the problem of uncertainty in numerical types of mixed data, random perturbation is applied to the user data at the attribute category level. The server then performs clustering on the perturbed data, and density threshold and disturbance probability are introduced to update the cluster point set iteratively. In addition, a new distance calculation formula is defined in combination with attribute weights to ensure the availability of data. The experimental results show that LDPIA algorithm achieves better privacy protection and availability simultaneously.