从水平分布的数据库中安全频繁的项目集挖掘

K. Harikrishnasairaj, V. Prasad
{"title":"从水平分布的数据库中安全频繁的项目集挖掘","authors":"K. Harikrishnasairaj, V. Prasad","doi":"10.1109/I2C2.2017.8321937","DOIUrl":null,"url":null,"abstract":"We propose a protocol for hiding the infrequent itemsets in horizontally distributed databases. Databases are horizontally partitioned when they are distributed among different players. Each player holds identical schema, but have information on different objects. Mining of frequent itemsets existed in databases of different players cause sensitive data leakage from one player to another player or to any third party. Mining should not reveal any players locally supported frequent itemsets among themselves or any third party. In order to maintain privacy, security is needed. We can provide the partial security to the item sets by removing infrequent subsets from the candidate item sets. In this thesis, a protocol for deriving frequent itemsets from horizontally distributed databases which does not leak secret information of the participating players in mining has been implemented. This protocol uses Fast Distributed Mining (FDM) algorithm which is given by Cheungetalet.el[3]. FDM algorithm uses the technique of Apriori[1] Algorithm to mine the frequent itemsets from distributed environment.","PeriodicalId":288351,"journal":{"name":"2017 International Conference on Intelligent Computing and Control (I2C2)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Secure frequent itemset mining from horizontally distributed databases\",\"authors\":\"K. Harikrishnasairaj, V. Prasad\",\"doi\":\"10.1109/I2C2.2017.8321937\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a protocol for hiding the infrequent itemsets in horizontally distributed databases. Databases are horizontally partitioned when they are distributed among different players. Each player holds identical schema, but have information on different objects. Mining of frequent itemsets existed in databases of different players cause sensitive data leakage from one player to another player or to any third party. Mining should not reveal any players locally supported frequent itemsets among themselves or any third party. In order to maintain privacy, security is needed. We can provide the partial security to the item sets by removing infrequent subsets from the candidate item sets. In this thesis, a protocol for deriving frequent itemsets from horizontally distributed databases which does not leak secret information of the participating players in mining has been implemented. This protocol uses Fast Distributed Mining (FDM) algorithm which is given by Cheungetalet.el[3]. FDM algorithm uses the technique of Apriori[1] Algorithm to mine the frequent itemsets from distributed environment.\",\"PeriodicalId\":288351,\"journal\":{\"name\":\"2017 International Conference on Intelligent Computing and Control (I2C2)\",\"volume\":\"179 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Intelligent Computing and Control (I2C2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/I2C2.2017.8321937\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Intelligent Computing and Control (I2C2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/I2C2.2017.8321937","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

提出了一种隐藏水平分布数据库中不频繁项集的协议。当数据库分布在不同的参与者之间时,它们是水平分区的。每个玩家拥有相同的图式,但拥有不同对象的信息。挖掘存在于不同玩家数据库中的频繁项集会导致敏感数据从一个玩家泄露到另一个玩家或任何第三方。挖掘不应该透露任何玩家本地支持的频繁道具集在他们自己或任何第三方之间。为了维护隐私,安全是必要的。我们可以通过从候选项集中删除不频繁的子集来为项集提供部分安全性。本文实现了一种从水平分布数据库中提取频繁项集的协议,该协议不会泄露挖掘参与方的机密信息。该协议采用Cheungetalet.el[3]给出的快速分布式挖掘(Fast Distributed Mining, FDM)算法。FDM算法利用Apriori[1]算法的技术从分布式环境中挖掘频繁项集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Secure frequent itemset mining from horizontally distributed databases
We propose a protocol for hiding the infrequent itemsets in horizontally distributed databases. Databases are horizontally partitioned when they are distributed among different players. Each player holds identical schema, but have information on different objects. Mining of frequent itemsets existed in databases of different players cause sensitive data leakage from one player to another player or to any third party. Mining should not reveal any players locally supported frequent itemsets among themselves or any third party. In order to maintain privacy, security is needed. We can provide the partial security to the item sets by removing infrequent subsets from the candidate item sets. In this thesis, a protocol for deriving frequent itemsets from horizontally distributed databases which does not leak secret information of the participating players in mining has been implemented. This protocol uses Fast Distributed Mining (FDM) algorithm which is given by Cheungetalet.el[3]. FDM algorithm uses the technique of Apriori[1] Algorithm to mine the frequent itemsets from distributed environment.
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