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We present an approach to differentially private computation in which onedoes not scale up the magnitude of noise for challenging queries, but ratherscales down the contributions of challenging records. While scaling down allrecords uniformly is equivalent to scaling up the noise magnitude, we show thatscaling records non-uniformly can result in substantially higher accuracy bybypassing the worst-case requirements of differential privacy for the noisemagnitudes. This paper details the data analysis platform wPINQ, whichgeneralizes the Privacy Integrated Query (PINQ) to weighted datasets. Using afew simple operators (including a non-uniformly scaling Join operator) wPINQcan reproduce (and improve) several recent results on graph analysis andintroduce new generalizations (e.g., counting triangles with given degrees). Wealso show how to integrate probabilistic inference techniques to synthesizedatasets respecting more complicated (and less easily interpreted)measurements.

Calibrating Data to Sensitivity in Private Data Analysis