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Query Auditing Against Partial Disclosure

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Query Auditing Against Partial Disclosure

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dc.contributor.author Motgi, Mayur en_US
dc.date.accessioned 2009-09-16T18:16:56Z
dc.date.available 2009-09-16T18:16:56Z
dc.date.issued 2009-09-16T18:16:56Z
dc.date.submitted January 2009 en_US
dc.identifier.other DISS-10222 en_US
dc.identifier.uri http://hdl.handle.net/10106/1672
dc.description.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. en_US
dc.description.sponsorship Zhang, Nan en_US
dc.language.iso EN en_US
dc.publisher Computer Science & Engineering en_US
dc.title Query Auditing Against Partial Disclosure en_US
dc.type M.S. en_US
dc.contributor.committeeChair Zhang, Nan en_US
dc.degree.department Computer Science & Engineering en_US
dc.degree.discipline Computer Science & Engineering en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level masters en_US
dc.degree.name M.S. en_US
dc.identifier.externalLink http://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=1592
dc.identifier.externalLinkDescription Link to Research Profiles

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