Hiding Association Rules by Using Confidence and Support

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A framework for high-accuracy privacy-preserving mining (2004, background): “Finally, the problem addressed in [10, 11 , 22, 23] is how to prevent sensitive rules from being inferred by the data miner ‐ this work is complementary to ours since it addresses concerns about output privacy, whereas our focus is on the privacy of the input data.”
Maintaining Data Privacy in Association Rule Mining (2002, background): “Concurrently with our work, the issue of maintaining privacy in association rule mining has attracted considerable attention over the last year [14, 5, 6 , 15, 19, 11, 8]. The problem addressed in [14, 5, 6, 15] is how to prevent sensitive rules from being inferred by the data miner { the proposed ”
Mining Association Rules under Privacy Constraints (2008, methodology): “The above work was extended in [15] to achieve hiding by also using the the confidence criterion.”