Privacy Aware Experimentation over Sensitive Groups: A General Chi Square Approach
Privacy Aware Experimentation over Sensitive Groups: A General Chi Square Approach
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针对敏感群体的隐私意识实验：通用卡方方法
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Ryan M. Rogers
中科院分区：
文献类型：
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作者：
R. Friedberg;Ryan M. Rogers
我们研究了一种新的隐私模型，其中用户属于某些敏感群体，我们希望对不同群体之间的结果是否存在显着差异进行统计推断。特别是，我们不认为用户的结果是敏感的，而只是某些组的成员资格。这与以前的工作不同，以前的工作考虑了当地私营的统计检验，结果和群体共同私有化，以及私营的A/B检验，其中群体被认为是公共的（对照组和治疗组），而结果私有化。我们涵盖了几个不同的设置的假设检验后，组成员已被私有化的样本，包括二进制和真实的值的结果。我们采用了在不同隐私模型的假设检验的其他作品中使用的广义$\chi^2 $测试框架，这使我们能够用一个统一的方法来覆盖$Z$-测试，$\chi^2 $独立性测试，t-测试和ANOVA测试。当考虑两个群体，我们得出的置信区间的真实差异的手段，并显示传统的方法计算置信区间错过了真正的差异时，隐私的介绍。对于两个以上的群体，我们考虑了几种机制，私有化的组成员资格，表明我们可以提高统计能力的传统测试，忽略了由于隐私的噪音。我们还考虑应用于私人A/B测试，以确定对照组和治疗组之间敏感组的平均值差异是否存在显著变化。
We study a new privacy model where users belong to certain sensitive groups and we would like to conduct statistical inference on whether there is significant differences in outcomes between the various groups. In particular we do not consider the outcome of users to be sensitive, rather only the membership to certain groups. This is in contrast to previous work that has considered locally private statistical tests, where outcomes and groups are jointly privatized, as well as private A/B testing where the groups are considered public (control and treatment groups) while the outcomes are privatized. We cover several different settings of hypothesis tests after group membership has been privatized amongst the samples, including binary and real valued outcomes. We adopt the generalized $\chi^2$ testing framework used in other works on hypothesis testing in different privacy models, which allows us to cover $Z$-tests, $\chi^2$ tests for independence, t-tests, and ANOVA tests with a single unified approach. When considering two groups, we derive confidence intervals for the true difference in means and show traditional approaches for computing confidence intervals miss the true difference when privacy is introduced. For more than two groups, we consider several mechanisms for privatizing the group membership, showing that we can improve statistical power over the traditional tests that ignore the noise due to privacy. We also consider the application to private A/B testing to determine whether there is a significant change in the difference in means across sensitive groups between the control and treatment.
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影响因子：
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作者：
Wang, Yue;Kifer, Daniel;Lee, Jaewoo
通讯作者：
Lee, Jaewoo
DOI：
10.1145/3319535.3339821
发表时间：
2019
期刊：
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS
影响因子：
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作者：
Couch, Simon;Kazan, Zeki;Shi, Kaiyan;Bray, Andrew;Groce, Adam
通讯作者：
Groce, Adam
DOI：
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发表时间：
2022
期刊：
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (AITSTAS
影响因子：
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作者：
Ferrando, Cecilia;Wang, Shufan;Sheldon, Daniel
通讯作者：
Sheldon, Daniel
DOI：
10.48550/arxiv.2203.00194
发表时间：
2022-03
期刊：
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影响因子：
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作者：
V. Feldman;Jelani Nelson;Huy L. Nguyen;Kunal Talwar
通讯作者：
V. Feldman;Jelani Nelson;Huy L. Nguyen;Kunal Talwar
DOI：
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发表时间：
2020
期刊：
Advances in Neural Information Processing Systems
影响因子：
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作者：
Canonne, Clement;Kamath, Guatam;McMillan, Audra;Ullman, Jonathan;Zakynthinou, Lydia
通讯作者：
Zakynthinou, Lydia