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

作　　者：Yousheng ZHOU Zhonghan WANG Yuanni LIU 

机构地区：[1]School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400000,China [2]School of Cyber Security and Information Law,Chongqing University of Posts and Telecommunications,Chongqing 400000,China

出　　处：《Chinese Journal of Electronics》2024年第6期1421-1434,共14页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.62272076)。

摘　　要：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.

关 键 词：Edge computing Privacy protection Local differential privacy Attribute weight Iterative clustering 

分 类 号：TN929.5[电子电信—通信与信息系统] TP309[电子电信—信息与通信工程]