Abstract
Pattern mining methods help to extract valuable information from a large dataset. The extraction of knowledge might result in the risk of privacy issues. Some potential information might disclosure the insights about customers’ behaviors. This leads us to the issue of privacy-preserving data mining (PPDM) that hides sensitive information as much as possible but remains valid information for the further knowledge discovery methods. In this paper, we first propose a sanitization approach for hiding the sensitive periodic frequent patterns. The proposed method utilizes the Term Frequency and Inverse Document Frequency (TF-IDF) to select the transactions and items for sanitization based on the user-defined sensitive periodic frequent patterns. The designed approach can select the victim items in the transactional database for data sanitization. Experimental results showed that the model can perform better for sparse and dense datasets under different user-defined thresholds.
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Ahmed, U., Lin, J.CW., Fournier-Viger, P. (2021). Privacy Preservation of Periodic Frequent Patterns Using Sensitive Inverse Frequency. In: Kiran, R.U., Fournier-Viger, P., Luna, J.M., Lin, J.CW., Mondal, A. (eds) Periodic Pattern Mining . Springer, Singapore. https://doi.org/10.1007/978-981-16-3964-7_12
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DOI: https://doi.org/10.1007/978-981-16-3964-7_12
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