Database anonymization and protections of sensitive attributes
Hsu, Shih-Ying Peter
Master of Science
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The importance of database anonymization has become increasingly critical for organizations that publish their database to the public. Current security measures for anonymization poses different manner of drawbacks. k-anonymity is prone to many varieties of attack; !-diversity does not work well with categorical or numerical attributes; t-closeness erases too much information in the database. Moreover, some measures of information loss are designed for anonymization measure, such as k-anonymity, where sensitive attributes do not play a part in measuring database's security. Not measuring the re-distribution of sensitive attributes will result in an underestimate for information loss such as 1- diversity or t-closeness which intentionally tries removing the association between non-sensitive attributes and sensitive attributes for better protecting individuals from being indentified. This thesis provides a more generalized version of !-diversity that will better protect categorical attributes and numerical attributes and analyzes the effectiveness and complexity of our new security scheme. Another focus of this thesis is to design a better approach of measuring information loss and lay down a new standard for evaluating information loss on security measures such as 1- diversity and t-closeness and quantify actual information loss from deliberately hiding relations between non-sensitive attributes and sensitive attributes. This new standard of information loss measure should provide a better estimation of the data mining potential remained in a generalized database. This thesis also proves that unlike k-anonymity which can be solved in polynomial time when k=2. 1-diversity in fact remains NP-Hard in the special case where 1=2, and even when there are only 2 possible sensitive attributes in the alphabet.