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State-of-the-art in privacy preserving data mining
We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classification hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also...
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Published in: | SIGMOD record 2004-03, Vol.33 (1), p.50-57 |
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Language: | English |
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cited_by | cdi_FETCH-LOGICAL-c342t-b1f1bbc8762d1177fd345dc77d7d8581f17cfad925a9e330854b8f1b36daa4313 |
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container_end_page | 57 |
container_issue | 1 |
container_start_page | 50 |
container_title | SIGMOD record |
container_volume | 33 |
creator | Verykios, Vassilios S. Bertino, Elisa Fovino, Igor Nai Provenza, Loredana Parasiliti Saygin, Yucel Theodoridis, Yannis |
description | We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classification hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classification hierarchy. A brief evaluation is performed, and some initial conclusions are made. |
doi_str_mv | 10.1145/974121.974131 |
format | article |
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language | eng |
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source | Association for Computing Machinery:Jisc Collections:ACM OPEN Journals 2023-2025 (reading list) |
title | State-of-the-art in privacy preserving data mining |
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