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We consider the problem of the private release of statistics (like aggregatepayrolls) where it is critical to preserve the contribution made by a smallnumber of outlying large entities. We propose a privacy formalism, per-recordzero concentrated differential privacy (PzCDP), where the privacy lossassociated with each record is a public function of that record's value. Unlikeother formalisms which provide different privacy losses to different records,PzCDP's privacy loss depends explicitly on the confidential data. We define ourformalism, derive its properties, and propose mechanisms which satisfy PzCDPthat are uniquely suited to publishing skewed or heavy-tailed statistics, wherea small number of records contribute substantially to query answers. Thistargeted relaxation helps overcome the difficulties of applying standard DP tothese data products.

Privately Answering Queries on Skewed Data via Per Record Differential Privacy