@inproceedings{e3f2723989024a1882791a1fb1c1a6cf,
title = "Hiding emerging patterns with local recoding generalization",
abstract = "Establishing strategic partnership often requires organizations to publish and share meaningful data to support collaborative business activities. An equally important concern for them is to protect sensitive patterns like unique emerging sales opportunities embedded in their data. In this paper, we contribute to the area of data sanitization by introducing an optimization-based local recoding methodology to hide emerging patterns from a dataset but with the underlying frequent itemsets preserved as far as possible. We propose a novel heuristic solution that captures the unique properties of hiding EPs to carry out iterative local recoding generalization. Also, we propose a metric which measures (i) frequentitemset distortion that quantifies the quality of published data and (ii) the degree of reduction in emerging patterns, to guide a bottom-up recoding process. We have implemented our proposed solution and experimentally verified its effectiveness with a benchmark dataset.",
keywords = "Data sanitization, Emerging patterns, Frequent itemsets, Pattern hiding",
author = "Cheng, {Michael W.K.} and CHOI, {Koon Kau} and CHEUNG, {Kwok Wai}",
note = "Copyright: Copyright 2011 Elsevier B.V., All rights reserved.; 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2010 ; Conference date: 21-06-2010 Through 24-06-2010",
year = "2010",
doi = "10.1007/978-3-642-13657-3_19",
language = "English",
isbn = "3642136567",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "158--170",
booktitle = "Advances in Knowledge Discovery and Data Mining - 14th Pacific-Asia Conference, PAKDD 2010, Proceedings",
edition = "PART 1",
}