Abstract #302031

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JSM 2003 Abstract #302031
Activity Number: 342
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 9:00 AM to 10:50 AM
Sponsor: Section on Government Statistics
Abstract - #302031
Title: Statistical Sensitive Data Protection and Inference Prevention with Decision Tree Methods
Author(s): Liwu Chang*+
Companies: Naval Research Laboratory
Address: Mail Code 5540, Washington, DC, 20375-0001,
Keywords: data confidentiality ; statistical inference ; information hiding ; data disclosure ; decision tree ; ramification
Abstract:

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 nonsensitive data, we have the inference problem. We consider inference as correct classification and approach it with decision tree methods. As in our previous work, sensitive data are viewed as classes of those test data and nonsensitive data are the rest attribute values. In general, however, sensitive data may not be associated with one attribute (i.e., the class), but are distributed among many attributes. We present a generalized decision tree (GDT) method for distributed sensitive data. GDT 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 and also discuss other statistical attacks.

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