Fast Classification Approach of Support Vector Machine with Privacy Preservation

HU Wen-jun; WANG Shi-tong

doi:10.3969/j.issn.0372-2112.2012.02.012

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Fast Classification Approach of Support Vector Machine with Privacy Preservation

更新时间：2025-07-16

- Fast Classification Approach of Support Vector Machine with Privacy Preservation
- Acta Electronica Sinica Vol. 40, Issue 2, Pages: 280-286(2012)
- 作者机构：
  
  1. 江南大学数字媒体学院,江苏,无锡,214122
  2. 湖州师范学院信息与工程学院,浙江,湖州,313000
  3. 江南大学数字媒体学院江苏无锡,214122
  4. 湖州师范学院信息与工程学院浙江湖州,313000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2012.02.012
  CLC： TP391
- Published：2012
- 稿件说明：
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HU Wen-jun, WANG Shi-tong. Fast Classification Approach of Support Vector Machine with Privacy Preservation[J]. Acta Electronica Sinica, 2012, 40(2): 280-286.
DOI：

HU Wen-jun, WANG Shi-tong. Fast Classification Approach of Support Vector Machine with Privacy Preservation[J]. Acta Electronica Sinica, 2012, 40(2): 280-286. DOI： 10.3969/j.issn.0372-2112.2012.02.012.

摘要

许多核分类方法的决策函数可以表示为支持向量的组合

如SVM

而支持向量含有非常重要的隐私信息

因此

在分类决策时可能会暴露此类信息

同时分类速度受限于支持向量的个数

如SVM的分类复杂度为

SVs

|).为解决上述两个问题

本文基于最小包含球球心在原始空间中的代理原像

提出了一种隐藏支持向量信息并能快速实现分类的SVM方法

称为隐私保护的快速SVM分类方法(Fast Classification Approach of SVM with Privacy Preservation

FCA-SVM

WPP

).同时提供了两种求解代理球心原像的方法

分别称为QP解法和直接解法.UCI和PIE人脸数据集的实验结果表明

本文方法可解决上述两个问题并具有较好的效果.

Abstract

The decision functions of various kernelized classification methods can be expressed as a combination of Support Vectors (SVs)

i.e.SVM

which contain the individual privacy information

so this information will be released during detecting unknown samples.Meanwhile

the amount of SVs limits classification speed

i.e.the computational time complexity of SVM is

SVs

|).For overcoming the above drawbacks

a fast classification approach of SVM with privacy preservation is proposed

which is based on the agent preimage of the center of minimum enclosing ball (MEB)

and two preimage-finding methods are presented in this paper

called QP-based sol

ution and direct solution respectively.Experimental results on UCI and PIE face image demonstrate that two drawbacks as above can not only be solved

but also the obtained effectiveness of the proposed method is competitive.

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