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Additionally, we address the important matter of privacy-preserving data mining by ensuring that the density estimate fulfills privacy-related properties. To ...
Additionally, we address the important matter of privacy-preserving data mining by ensuring that the density estimate fulfills privacy-related properties. To ...
This paper directly exploits the structure of the density estimates to extract frequent itemsets and addresses the important matter of privacy-preserving ...
PDF | On Aug 1, 2017, Michael Geilke and others published Privacy-Preserving Pattern Mining on Online Density Estimates | Find, read and cite all the ...
Jun 4, 2021 · In this work, we investigate privacy-preserving density-based clustering which is, for example, used in financial analytics and medical diagnosis.
Bibliographic details on Privacy-Preserving Pattern Mining on Online Density Estimates.
Privacy-preserving data mining (PPDM) refers to techniques used to enhance privacy while extracting valuable information from data mining processes.
Bibtex. Michael Geilke and Stefan Kramer Privacy-Preserving Pattern Mining on Online Density Estimates In: Proceedings of the International Conference on Big ...
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In this paper, we propose a privacy-preserving framework using sequential pattern mining in distributed data sources.
Missing: Online Density Estimates.
Abstract—Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise ...