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Abstract Details
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Activity Number:
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8
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Government Statistics
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| Abstract - #303750 |
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Title:
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Protecting Interrelated Time Series with Synthetic Data Models
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Author(s):
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Matthew John Schneider*+
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Companies:
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Cornell University
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Address:
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Department of Statistical Science, Ithaca, NY, 14853-3801,
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Keywords:
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Statistical Disclosure Limitation ;
differential privacy ;
Quarterly Workforce Indicators ;
Generalized Linear Mixed Models ;
time series ;
synthetic data
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Abstract:
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Statistical Disclosure Limitation and formal privacy modeling are applied to the interrelated, hierarchical, and temporal Quarterly Workforce Indicators. Synthetic data methods capture the essential features of confidential data while protecting the dependent data. Bootstrapping procedures are used to address the problem of dependent data that is not independent and identically distributed which is an issue for some differential privacy algorithms. Generalized Linear Mixed Models that handle time series and sparse counts are also investigated and improve on methods currently in use. Finally, probabilities of model failure are calculated over feasible ranges of protection.
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Authors who are presenting talks have a * after their name.
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