Private Rate-Double-Robust Inference
私有率双稳健推断
Máté Kormos, Aad van der Vaart
AI总结 本文通过局部隐私机制注入噪声保护个体隐私,同时利用率双稳健性实现目标参数的无偏和半参数有效推断,并开发了私有化非参数和参数 nuisance 估计方法。
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我们协调了隐私保护和率双稳健推断。个体隐私通过局部隐私机制得到保护:向敏感数据注入噪声,仅揭示用于推断的噪声数据。因此,隐私保护阻碍了推断。相比之下,当目标参数的估计量的大样本偏差由另外两个 nuisance 参数的估计误差之间的权衡表征时,该参数的推断是率双稳健的。因此,率双稳健性促进了推断。我们协调的起点是一类由无限维线性索引和低维非线性回归索引的率双稳健目标参数。这包括因果参数等。为了私有地推断这些目标,我们展示了合适的隐私机制如何将敏感数据模型的半参数性质转移到私有设置中。率双稳健性被转移,从而实现了对目标参数的局部私有、无偏和半参数有效推断。最后,我们将一般的非参数 nuisance 估计量转化为私有估计量,这些估计量继承了其非私有对应物的收敛性质。对于参数 nuisance 模型,我们开发了一种私有矩估计方法及其大样本推断理论。
We reconcile privacy protection and rate-double-robust inference. The privacy of individuals is protected by a local privacy mechanism: injecting noise into their sensitive data, revealing only the noisy data for inference. Hence, privacy protection hinders inference. In contrast, the inference of a target parameter is rate-double-robust when the large-sample bias of an estimator of the parameter is characterised by a trade-off between the estimation errors of two other, nuisance, parameters. Hence, rate-double-robustness facilitates inference. Our starting point of reconciliation is a class of rate-double-robust target parameters indexed linearly by an infinite-dimensional and nonlinearly by a low-dimensional regression. Among others, this includes causal parameters. To infer these targets privately, we show how suitable privacy mechanisms transfer the semiparametric properties of the sensitive-data model to the private setting. Rate-double-robustness is transferred, enabling locally-private, unbiased and semiparametrically efficient inference of our target parameters. Finally, we transform general nonparametric nuisance estimators into private ones, which inherit convergence properties of their nonprivate counterparts. For parametric nuisance models, we develop a private method-of-moments estimator and its large-sample inference theory.