Identity Disclosure Protection: A Data Reconstruction Approach for Preserving Privacy in Data Mining

Identity disclosure is one of the most serious privacy concerns in today’s information age. A wellknow 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 kanonymity 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 kanonymity constraint.