CRM系统数据挖掘中的隐私保护算法

Shashidhar Virupaksha, G. Sahoo, A. Vasudevan
{"title":"CRM系统数据挖掘中的隐私保护算法","authors":"Shashidhar Virupaksha, G. Sahoo, A. Vasudevan","doi":"10.1109/ICCSP.2014.6950121","DOIUrl":null,"url":null,"abstract":"Organizations have a huge customer base and thus they use data mining tools to study their customers. However there is risk of sensitive information about individuals which can be gained also during this process. Hence data that is used for data mining has to be protected. There are some privacy protection algorithms which ensure privacy and protect data. These algorithms preserve privacy but data mining results significantly. In this paper we propose a clustering based noise addition that not only preserves privacy but also ensures effective data mining. Data characteristics are identified using clustering technique and noise is added within the clusters thus retaining the data characteristics.","PeriodicalId":149965,"journal":{"name":"2014 International Conference on Communication and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Privacy preservation algorithm in data mining for CRM systems\",\"authors\":\"Shashidhar Virupaksha, G. Sahoo, A. Vasudevan\",\"doi\":\"10.1109/ICCSP.2014.6950121\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Organizations have a huge customer base and thus they use data mining tools to study their customers. However there is risk of sensitive information about individuals which can be gained also during this process. Hence data that is used for data mining has to be protected. There are some privacy protection algorithms which ensure privacy and protect data. These algorithms preserve privacy but data mining results significantly. In this paper we propose a clustering based noise addition that not only preserves privacy but also ensures effective data mining. Data characteristics are identified using clustering technique and noise is added within the clusters thus retaining the data characteristics.\",\"PeriodicalId\":149965,\"journal\":{\"name\":\"2014 International Conference on Communication and Signal Processing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Communication and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSP.2014.6950121\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Communication and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSP.2014.6950121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

组织拥有庞大的客户群,因此他们使用数据挖掘工具来研究他们的客户。然而,在这一过程中也可能获得有关个人的敏感信息。因此,必须保护用于数据挖掘的数据。有一些隐私保护算法可以保证隐私和保护数据。这些算法保护隐私,但数据挖掘效果显著。本文提出了一种基于聚类的噪声添加方法,既保护了隐私,又保证了数据挖掘的有效性。采用聚类技术识别数据特征,并在聚类中加入噪声,从而保留数据特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Privacy preservation algorithm in data mining for CRM systems
Organizations have a huge customer base and thus they use data mining tools to study their customers. However there is risk of sensitive information about individuals which can be gained also during this process. Hence data that is used for data mining has to be protected. There are some privacy protection algorithms which ensure privacy and protect data. These algorithms preserve privacy but data mining results significantly. In this paper we propose a clustering based noise addition that not only preserves privacy but also ensures effective data mining. Data characteristics are identified using clustering technique and noise is added within the clusters thus retaining the data characteristics.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信