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Activity Number:
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184
- Contributed Poster Presentations: Korean International Statistical Society
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Type:
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Contributed
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Date/Time:
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Monday, July 29, 2019 : 10:30 AM to 12:20 PM
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Sponsor:
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Korean International Statistical Society
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Abstract #306647
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Title:
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Differentially Private Goodness-of-Fit Test for Continuous Random Variable
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Author(s):
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Seungwoo Kwak* and Jeongyoun Ahn and Cheolwoo Park and Jaewoo Lee
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Companies:
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and University of Georgia and University of Georgia and University of Georgia
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Keywords:
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Differential Privacy;
Goodness-of-fit test;
Machine Learning
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Abstract:
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A goodness-of-fit test is a statistical hypothesis test to see how well sample data fit a distribution. The data may contain sensitive individual data. Thus, we need to design statistical tests that guarantee the privacy of subjects in the data. Differential privacy is a statistical technique that provides means to maximize the utility of queries from databases while measuring the privacy impact on individuals whose information is in the database. We develop a goodness-of-fit test by discretizing a continuous random variable and adopting results on differentially private goodness-of-fit tests for discrete data.
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Authors who are presenting talks have a * after their name.
