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Evaluating the Privacy Implications of Frequent Itemset Disclosure
Abstract : Frequent itemset mining is a fundamental data analytics task. In many cases, due to privacy concerns, only the frequent itemsets are released instead of the underlying data. However, it is not clear how to evaluate the privacy implications of the disclosure of the frequent itemsets. Towards this, in this paper, we define the k-distant-IFM-solutions problem, which aims to find k transaction datasets whose pair distance is maximized. The degree of difference between the reconstructed datasets provides a way to evaluate the privacy risk. Since the problem is NP-hard, we propose a 2-approximate solution as well as faster heuristics, and evaluate them on real data.
Keywords :
Inverse Frequent itemset Mining
Column generation
Cited literature [14 references]
https://hal.inria.fr/hal-01649007
Contributor : Hal Ifip <>
Submitted on : Monday, November 27, 2017 - 10:31:49 AM
Last modification on : Friday, August 9, 2019 - 3:24:28 PM
Contributor : Hal Ifip <>
Submitted on : Monday, November 27, 2017 - 10:31:49 AM
Last modification on : Friday, August 9, 2019 - 3:24:28 PM
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449885_1_En_34_Chapter.pdf
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Distributed under a Creative Commons Attribution 4.0 International License