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Abstract

Nowadays people are sharing their information in digitally, and their information plays the significant role in public sectors and business world assist in understanding the social behavior, statistical analysis and drafting the business policies for future direction. At the same time, users’ personal information should be preserved from adversaries while data publishing. Privacy preservation is one of the main agenda of data publishing, K-anonymity and its families likel-diversity and t-closeness which are the most familiar models to preserve the sensitive information of individuals in data publishing. Besides, data reliability is also the main factor of reliable anonymized data. Various anonymization techniques which are performed the data protection against attack models; however, the privacy models satisfy the privacy preservation effectively. The data utilization is also a vital account to achieve the purpose of data publishing. Moreover; Publisher focuses on the utilization of anonymized dataset that might be helpful to analyze the meaningful data. Otherwise, the potency of anonymization leads to diminishing the data utility of anonymized dataset and weakening the purpose of data publishing. In this proposed method aims to improve the utilization of anonymized data in data publishing with minimal information loss based on fuzzy alpha-cuts. Triangular membership function has applied over in the proposed method to generalize the quasi-identifiers by constructed alpha cut sets and taxonomy tree. The evaluation results are compared with an existing model concerning information loss and time execution time metrics. The performance of the proposed method is encouraging that based on the experimental results.

Keywords

Fuzzy logic fuzzy inference system data privacy fuzzy generalization anonymity

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Fuzzy based cell generalization to improve the data utility with minimal loss of information

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