提议-过滤-发布：通过私有过滤将噪声缩放到典型敏感度
Propose-Filter-Release: Scaling Noise to Typical Sensitivity via Private Filtering
Moshe Shenfeld, Eliad Tsfadia, Katrina Ligett
AI 速览：提出 propose-filter-release（PFR）框架，通过私有过滤输入，把仅对“典型记录”提供保护的友好型差分隐私机制，转化为对任意输入都满足标准差分隐私的机制。
An estimator can be accurate and stable on typical data, yet sensitive to a few possible records. Differentially private mechanisms commonly scale their noise to the worst-case influence of a single record, which lets these records dictate the noise even when the remaining data would allow far less. Removing them first would restore the smaller scale, but the removal must itself be private. We introduce \emph{propose-filter-release} (PFR), which, by privately filtering its input, turns a mechanism that protects the addition or removal of a typical record, one that fits the remaining data, into one that is differentially private in the standard sense, on every input. Using our novel filtering step, PFR requires only a concave friendliness score, which measures how well a record fits a dataset, and a release mechanism that is friendly-DP, that is, private when the changed record fits the remaining data. For every such score and release mechanism, it guarantees differential privacy under public conditions on the number of records, so it is a general privacy primitive, comparable to propose-test-release, and FriendlyCore can be viewed as one of its implementations. We illustrate the generality of PFR by instantiating it for scalar statistics, principal subspace estimation, the KL divergence of user histograms, the effective resistances of a graph, and treatment-rule selection.