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- W2410549043 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." @default.
- W2410549043 created "2016-06-24" @default.
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- W2410549043 date "2002-01-01" @default.
- W2410549043 modified "2023-09-25" @default.
- W2410549043 title "Privacy preserving association rule mining in vertically partitioned data" @default.
- W2410549043 doi "https://doi.org/10.1145/775107.775142" @default.
- W2410549043 hasPublicationYear "2002" @default.
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