Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Fast Algorithms for Mining Emerging Patterns
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian approach to use emerging patterns for classification
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Privacy preserving frequent itemset mining
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Random-data perturbation techniques and privacy-preserving data mining
Knowledge and Information Systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
World Wide Web
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
A MaxMin approach for hiding frequent itemsets
Data & Knowledge Engineering
Anonymizing transaction databases for publication
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies
IEEE Transactions on Knowledge and Data Engineering
GUFI: A New Algorithm for General Updating of Frequent Itemsets
CSEWORKSHOPS '08 Proceedings of the 2008 11th IEEE International Conference on Computational Science and Engineering - Workshops
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Competing on Analytics: The New Science of Winning
Competing on Analytics: The New Science of Winning
On the tradeoff between privacy and utility in data publishing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques
Introduction to Privacy-Preserving Data Publishing: Concepts and Techniques
Exploiting maximal emerging patterns for classification
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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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.