Differentially Private Selection from Secure Distributed Computin. (arXiv:2306.04564v1 [cs.CR])

Given a collection of vectors $x^{(1)},\dots,x^{(n)} \in \{0,1\}^d$, the
selection problem asks to report the index of an "approximately largest" entry
in $x=\sum_{j=1}^n x^{(j)}$. Selection abstracts a host of problems--in machine
learning it can be used for hyperparameter tuning, feature selection, or to
model empirical risk minimization. We study selection under differential
privacy, where a released index guarantees privacy for each vectors. Though
selection can be solved with an excellent utility guarantee in the central
model of differential privacy, the …

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