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• W2007369524 abstract "Identity disclosure is one of the most serious privacy concerns in today's information age. A well-known method for protecting identity disclosure is k-anonymity. A dataset provides k-anonymity protection if the information for each individual in the dataset cannot be distinguished from at least k − 1 individuals whose information also appears in the dataset. There is a flaw in k-anonymity that would still allow an intruder to discern the confidential information of individuals in the anonymized data. To overcome this problem, we propose a data reconstruction approach to achieve k-anonymity protection in predictive data mining. In this approach, the potentially identifying attributes are first masked using aggregation (for numeric data) and swapping (for nominal data). A genetic algorithm technique is then applied to the masked data to find a good subset of it. This subset is then replicated to form the released dataset that satisfies the k-anonymity constraint." @default.
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• W2007369524 date "2009-12-01" @default.
• W2007369524 modified "2023-10-17" @default.
• W2007369524 title "Identity disclosure protection: A data reconstruction approach for privacy-preserving data mining" @default.
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• W2007369524 doi "https://doi.org/10.1016/j.dss.2009.07.003" @default.
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