Privacy-Preserving Statistical Quantitative Rules Mining

Weiwei Jing; Liusheng Huang; Yifei Yao; Weijiang Xu

2nd International ICST Conference on Scalable Information Systems

Research Article

Privacy-Preserving Statistical Quantitative Rules Mining

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Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/infoscale.2007.209,
    author={Weiwei Jing and Liusheng Huang and Yifei Yao and Weijiang Xu},
    title={Privacy-Preserving Statistical Quantitative Rules Mining},
    proceedings={2nd International ICST Conference on Scalable Information Systems},
    proceedings_a={INFOSCALE},
    year={2010},
    month={5},
    keywords={Privacy-Preserving Data Mining Statistical Quantitative rules},
    doi={10.4108/infoscale.2007.209}
}

Weiwei Jing
Liusheng Huang
Yifei Yao
Weijiang Xu
Year: 2010
Privacy-Preserving Statistical Quantitative Rules Mining
INFOSCALE
ICST
DOI: 10.4108/infoscale.2007.209

Weiwei Jing¹^,*, Liusheng Huang¹^,*, Yifei Yao¹^,*, Weijiang Xu²^,*

1: Depart.of Comp. Sci. & Tech., USTC NHPCC 416, East Campus USTC, Hefei, 230026, PRC Tel: 86-551-3602445
2: Depart.of Comp. Sci. & Tech., USTC NHPCC 416, East Campus USTC, Hefei, PRC Tel: 86-551-3602445

*Contact email: wwjing@mail.ustc.edu.cn, lshuang@ustc.edu.cn, yaoyifei@mail.ustc.edu.cn, wjxu@mail.ustc.edu.cn

Abstract

This paper considers the problem of mining Statistical Quantitative rules (SQ rules) without revealing the private information of parties who compute jointly and share distributed data. Based on several basic tools for Privacy-Preserving Data Mining (PPDM), including secure sum, secure mean and secure frequent itemsets, this paper presents two algorithms to accomplish privacy-preserving SQ rules mining over horizontally partitioned data. One is to securely compute confidence intervals for testing the significance of rules; the other is to securely discover SQ rules.

Keywords: Privacy-Preserving Data Mining Statistical Quantitative rules

Published: 2010-05-16
Modified: 2011-09-11

: http://dx.doi.org/10.4108/infoscale.2007.209

Privacy-Preserving Statistical Quantitative Rules Mining

Abstract

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