Mining frequent patterns securely in distributed system

被引：6

作者：

Wang, Jiahong

Fukasawa, Takuya

Urabe, Shintaro

Takata, Toyoo

Miyazaki, Masatoshi

机构：

[1] Project EF Corp, Tokyo 1030014, Japan

[2] Argo Solut Co Ltd, Sendai, Miyagi 9800811, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2006年 / E89D卷 / 11期

关键词：

data mining; distributed computing; privacy-preserving; performance evaluation;

D O I：

10.1093/ietisy/e89-d.11.2739

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data mining across different companies, organizations, online shops, or the likes is necessary so as to discover valuable shared patterns. associations, trends, or dependencies in their shared data. Privacy, however, is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement, in this paper we propose an effective distributed privacy-preserving data mining approach called SDDM. SDDM is characterized by its ability to resist collusion. Unless the number of colluding sites in a distributed system is larger than or equal to 4, privacy cannot be violated. Results of performance study demonstrated the effectiveness of SDDM.

引用

页码：2739 / 2747

页数：9

共 23 条

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