Differential Privacy on Edge Computing

IF 2.3 Q3 NANOSCIENCE & NANOTECHNOLOGY

IEEE Nanotechnology Magazine Pub Date : 2023-01-01 DOI:10.1109/mnano.2023.3316873

Xiyu Jiang, Yao-Tung Tsou, Sy-Yen Kuo

引用次数: 0

Abstract

This paper presents an overview of privacy protection, with a focus on differential privacy (DP), from the perspective of edge computing. It explores the application of DP in various associative analysis techniques, including heavy hitter mining, frequent itemset mining, and association rules mining, within the context of edge computing. The paper also highlights the current challenges and future research directions in this area, including differentially private hybrid models and federated learning. By examining the intersection of privacy protection and edge computing, this paper provides insights into the application of DP and its potential for preserving privacy in associative analysis tasks within edge computing environments.

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边缘计算中的差分隐私

本文从边缘计算的角度对隐私保护进行了概述，重点介绍了差分隐私(DP)。它探讨了DP在各种关联分析技术中的应用，包括在边缘计算背景下的重磅挖掘、频繁项集挖掘和关联规则挖掘。本文还强调了该领域目前面临的挑战和未来的研究方向，包括差异私有混合模型和联邦学习。通过研究隐私保护和边缘计算的交集，本文提供了对DP的应用及其在边缘计算环境中的关联分析任务中保护隐私的潜力的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Nanotechnology Magazine NANOSCIENCE & NANOTECHNOLOGY-

CiteScore

2.90

自引率

6.20%

发文量

期刊介绍： IEEE Nanotechnology Magazine publishes peer-reviewed articles that present emerging trends and practices in industrial electronics product research and development, key insights, and tutorial surveys in the field of interest to the member societies of the IEEE Nanotechnology Council. IEEE Nanotechnology Magazine will be limited to the scope of the Nanotechnology Council, which supports the theory, design, and development of nanotechnology and its scientific, engineering, and industrial applications.