Differential Privacy for Black-Box Statistical Analyses
Nitin Kohli · Paul Laskowski
Abstract
We formalize a notion of a privacy wrapper, defined as an algorithm that can take an arbitrary and untrusted script and produce an output with differential privacy guarantees. Our novel privacy wrapper design leverages subsets of data and halts to overcome the issue of unknowable sensitivity.
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