Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing

Marco Gaboardi, Hyun Lim, Ryan Rogers, Salil Vadhan
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2111-2120, 2016.

Abstract

Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables.

Cite this Paper

BibTeX
@InProceedings{pmlr-v48-rogers16, title = {Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing}, author = {Gaboardi, Marco and Lim, Hyun and Rogers, Ryan and Vadhan, Salil}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {2111--2120}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/rogers16.pdf}, url = {https://proceedings.mlr.press/v48/rogers16.html}, abstract = {Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables.} }
Endnote
%0 Conference Paper %T Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing %A Marco Gaboardi %A Hyun Lim %A Ryan Rogers %A Salil Vadhan %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-rogers16 %I PMLR %P 2111--2120 %U https://proceedings.mlr.press/v48/rogers16.html %V 48 %X Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables.
RIS
TY - CPAPER TI - Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing AU - Marco Gaboardi AU - Hyun Lim AU - Ryan Rogers AU - Salil Vadhan BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-rogers16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 2111 EP - 2120 L1 - http://proceedings.mlr.press/v48/rogers16.pdf UR - https://proceedings.mlr.press/v48/rogers16.html AB - Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables. ER -
APA
Gaboardi, M., Lim, H., Rogers, R. & Vadhan, S.. (2016). Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:2111-2120 Available from https://proceedings.mlr.press/v48/rogers16.html.

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