Privacy-preserving distributed mining of association rules on horizontally partitioned data
| Venue: | In The ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’02 |
| Citations: | 35 - 10 self |
BibTeX
@INPROCEEDINGS{Clifton_privacy-preservingdistributed,
author = {Christopher Clifton and Murat Kantarcioglu and Chris Clifton and Senior Member},
title = {Privacy-preserving distributed mining of association rules on horizontally partitioned data},
booktitle = {In The ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’02},
year = {},
pages = {24--31}
}
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Abstract
Abstract—Data mining can extract important knowledge from large data collections—but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. This paper addresses secure mining of association rules over horizontally partitioned data. The methods incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task. Index Terms—Data mining, security, privacy. æ
