Privacy Preserving Association Rule Mining in Vertically Partitioned Data (2002)
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| Venue: | In The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Citations: | 159 - 18 self |
BibTeX
@INPROCEEDINGS{Vaidya02privacypreserving,
author = {Jaideep Vaidya and Chris Clifton},
title = {Privacy Preserving Association Rule Mining in Vertically Partitioned Data},
booktitle = {In The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2002},
pages = {639--644}
}
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Abstract
Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.
