Private query release assisted by public data
International Conference on Machine Learning, 2020•proceedings.mlr.press
We study the problem of differentially private query release assisted by access to public
data. In this problem, the goal is to answer a large class $\mathcal {H} $ of statistical queries
with error no more than $\alpha $ using a combination of public and private samples. The
algorithm is required to satisfy differential privacy only with respect to the private samples.
We study the limits of this task in terms of the private and public sample complexities. Our
upper and lower bounds on the private sample complexity have matching dependence on …
data. In this problem, the goal is to answer a large class $\mathcal {H} $ of statistical queries
with error no more than $\alpha $ using a combination of public and private samples. The
algorithm is required to satisfy differential privacy only with respect to the private samples.
We study the limits of this task in terms of the private and public sample complexities. Our
upper and lower bounds on the private sample complexity have matching dependence on …
Abstract
We study the problem of differentially private query release assisted by access to public data. In this problem, the goal is to answer a large class of statistical queries with error no more than using a combination of public and private samples. The algorithm is required to satisfy differential privacy only with respect to the private samples. We study the limits of this task in terms of the private and public sample complexities. Our upper and lower bounds on the private sample complexity have matching dependence on the dual VC-dimension of . For a large category of query classes, our bounds on the public sample complexity have matching dependence on .
proceedings.mlr.press
以上显示的是最相近的搜索结果。 查看全部搜索结果