Query Auditing Against Partial Disclosure

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Title: Query Auditing Against Partial Disclosure
Author: Motgi, Mayur
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 .
URI: http : / /hdl .handle .net /10106 /1672
Date: 2009-09-16

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Query Auditing Against Partial Disclosure. Available electronically from http : / /hdl .handle .net /10106 /1672 .

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