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Proceedings ArticleDOI

Research on privacy preserving association rule mining a survey

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TLDR
The general principles and methods of privacy preserving association rule mining are analyzed and summarized, and an effective metrics for measuring side-effects resulted from privacy preserving process are introduced.
Abstract
Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Recently, there has been growing concern over the privacy implications of association rule mining. This paper described the basic concepts related to association rule mining, and analyzed and summarized the general principles and methods of privacy preserving association rule mining, and pointed out the drawback of the these methods. It also introduced an effective metrics for measuring side-effects resulted from privacy preserving process. Finally the present problems and directions for future research are discussed.

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Citations
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Patent

Method and apparatus for verifying processed data

TL;DR: In this article, a data processing result from a trustworthy party is verified by the data processing results from a requesting party or a processing party in response to receiving a request for verifying correctness of the result from the requesting party.
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Protecting Sensitive knowledge in association patterns mining

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References
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Proceedings ArticleDOI

Mining association rules between sets of items in large databases

TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Journal ArticleDOI

Mining association rules between sets of items in large databases

TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.
Journal ArticleDOI

Privacy-preserving data mining

TL;DR: This work considers the concrete case of building a decision-tree classifier from training data in which the values of individual records have been perturbed and proposes a novel reconstruction procedure to accurately estimate the distribution of original data values.
Journal ArticleDOI

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Journal ArticleDOI

Privacy Preserving Data Mining

TL;DR: This work considers a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information, and proposes a protocol that is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth.
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