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Database Anonymization Techniques with Focus on Uncertainty and Multi-Sensitive Attributes
Abstract
Publication of Data owned by various organizations for scientific research has the danger of sensitive information of respondents being disclosed. The policy of removal or encryption of identifiers cannot avoid the leakage of information through quasi-identifiers. So, several anonymization techniques like k-anonymity, l-diversity, and t-closeness have been proposed. However, uncertainty in data cannot be handled by these algorithms. One solution to this is to develop anonymization algorithms by using rough set based clustering algorithms like MMR, MMeR, SDR, SSDR, and MADE at the clustering stage of existing algorithms. Some of these algorithms handle both numerical and categorical data. In this chapter, the author addresses the database anonymization problem and briefly discusses k-anonymization methods. The primary focus is on the algorithms dealing with l-diversity of databases having single or multi-sensitive attributes. The author also proposes certain algorithms to deal with anonymization of databases with involved uncertainty. Also, the aim is to draw attention of researchers towards the various open problems in this direction.
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