|Title||Statistical Sensitive Data Protection And Inference Prevention with Decision Tree Methods|
|Publication Type||Conference Paper|
|Year of Publication||2003|
|Conference Name||Joint Statistical Meeting 2003|
We present a new approach for protecting sensitive data in a relational table (columns: attributes; rows: records). If sensitive data can be inferred by unauthorized users with non-sensitive data, we have the inference problem. We consider inference as correct classification and approach it with decision tree methods. We present a generalized decision tree method for distributed sensitive data. This method takes in turn each attribute as the class and analyze the corresponding classification error. Attribute values that maximize an integrated error measure are selected for modification. Our analysis shows that modified attribute values can be restored and hence, sensitive data are not securely protected. This result implies that modified values must themselves be subjected to protection. We present methods for this ramified protection problem.
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