Mayur Motgi

Graduation Semester and Year

2009

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Nan Zhang

Abstract

Many government agencies, businesses, and nonprofit organizations need to collect, analyze, and report data about individuals in order to support their short-term and long-term planning activities. Statistical Databases therefore contain confidential information such as income, credit ratings, type of disease, or test scores of individuals. Such data are typically stored online and analyzed using sophisticated database management systems (DBMS) and software packages. On one hand, such database systems are expected to satisfy user requests of aggregate statistics related to non-confidential and confidential attributes. On the other hand, the system should be secure enough to guard against a user's ability to infer any confidential information related to a specific individual represented in the database. A major privacy threat is the adversarial inference of individual (private) tuples from aggregate query answers. Most existing work focuses on the exact disclosure problem, which is inadequate in practice. We propose a novel auditing algorithm for defending against partial disclosure. We introduce ENTROPY-AUDITING, an efficient query-auditing algorithm for partial disclosure that supports a mixture of common aggregate functions. In particular, we classify aggregate functions into two categories: MIN-like (e.g., MIN and MAX) and SUM-like (e.g., SUM and MEDIAN), and support a combination of them. Our proposed scheme utilizes an exact-auditing algorithm as a primitive function, and supports a combination of queries with various aggregate functions (e.g., SUM, MIN, MAX). We also present a detailed experimental evaluation of our PARTIAL-AUDITING approach.

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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