Functionally Private Approximations of Negligibly-Biased Estimators

André Madeira & S. Muthukrishnan
We study functionally private approximations. An approximation function $g$ is {\em functionally private} with respect to $f$ if, for any input $x$, $g(x)$ reveals no more information about $x$ than $f(x)$. Our main result states that a function $f$ admits an efficiently-computable functionally private approximation $g$ if there exists an efficiently-computable and negligibly-biased estimator for $f$. Contrary to previous generic results, our theorem is more general and has a wider application reach.We provide two distinct...
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