How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity. (arXiv:2210.03831v1 [cs.DS])

We develop a framework for efficiently transforming certain approximation
algorithms into differentially-private variants, in a black-box manner. Our
results focus on algorithms A that output an approximation to a function f of
the form $(1-a)f(x)-k <= A(x) <= (1+a)f(x)+k$, where 0<=a <1 is a parameter
that can be``tuned" to small-enough values while incurring only a poly blowup
in the running time/space. We show that such algorithms can be made DP without
sacrificing accuracy, as long as the function f has …

algorithm algorithms box functions