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Abstract Details
Activity Number:
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666
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Health Policy Statistics
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Abstract - #302956 |
Title:
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Assessing the Inferential Privacy of Synthetic Data
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Author(s):
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Gregory J. Matthews*+ and Ofer Harel and Robert H. Aseltine Jr.
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Companies:
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University of Connecticut and University of Connecticut and University of Connecticut
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Address:
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215 Glenbrook Road U-4120, Storrs, CT, 06269,
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Keywords:
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Synthetic Data ;
Health Policy ;
Statistical Disclosure Control
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Abstract:
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As the amount of data generated continues to increase, consideration of individuals' privacy is an increasing concern. As a result, there has been a vast quantity of research done on methods of statistical disclosure control (SDC). While many of these methods generate useful data from research, the levels of privacy offered by these methods is often hard to quantify. Synthetic data is one such SDC technique where the privacy provided by the method is difficult to quantify. Synthetic data is a technique for releasing microdata to the public while maintaining the privacy of the individual. The basic idea is to view sensitive values as missing and use imputation procedures to replace this data with reasonable values. From a privacy perspective, a major benefit is that identification disclosure is greatly reduced as the data are simulated. However, synthetic data are still susceptible to inferential privacy attacks. We explore the privacy of synthetic data using a proposed privacy metric based on the area under the receiver-operator characteristic curve.
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