A differential privacy-based classification system for edge computing in IoT

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Author(s)

  • Wanli Xue
  • Yiran Shen
  • Chengwen Luo
  • Wen Hu
  • Aruna Seneviratne

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)117-128
Journal / PublicationComputer Communications
Volume182
Online published8 Nov 2021
Publication statusPublished - 15 Jan 2022

Abstract

The blooming Internet of Things (IoTs) devices have brought many new types of sensing applications and methods to the traditional cloud-enabled IoTs framework. Hence, the new network framework becomes bidirectional gradually, in which IoTs devices can also perform moderate computation tasks instead of being solely used as data harvesters. This new coming framework known as edge computing significantly improves the traditional cloud-enabled network latency and dependency by shifting part of computation back to “local”. However, new security risks emerge when the edge computing shifts data and models back to the IoTs devices.
Acies, a differential privacy based privacy-preserving classification system for edge computing is proposed to secure the classification models offloaded to edge devices. Acies supports popular classifiers such as Nearest Neighborhood, Support Vector Machine and Sparse Representation Classifier with a variety of feature selection methods. According to our evaluation on different datasets, classification models with Acies can be private and maintain high utility. Acies achieves reliable privacy protection under reconstruction attacks with minimal impact on classification accuracy (2% ∽ 5%) only. Acies outperforms the naive input dataset perturbation methods by up to 30% higher classification accuracy when the privacy requirements of the applications is high (the privacy budget ε is less than 2).

Research Area(s)

  • Attack the edge, Decentralized, Edge-based classification, Internet of Things, Privacy-preserving

Citation Format(s)

A differential privacy-based classification system for edge computing in IoT. / Xue, Wanli; Shen, Yiran; Luo, Chengwen; Xu, Weitao; Hu, Wen; Seneviratne, Aruna.

In: Computer Communications, Vol. 182, 15.01.2022, p. 117-128.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review