Feb. 7, 2024, 5:10 a.m. | Apurv Shukla

cs.CR updates on arXiv.org arxiv.org

We consider a high-dimensional stochastic contextual linear bandit problem when the parameter vector is $s_{0}$-sparse and the decision maker is subject to privacy constraints under both central and local models of differential privacy. We present PrivateLASSO, a differentially private LASSO bandit algorithm. PrivateLASSO is based on two sub-routines: (i) a sparse hard-thresholding-based privacy mechanism and (ii) an episodic thresholding rule for identifying the support of the parameter $\theta$. We prove minimax private lower bounds and establish privacy and utility guarantees …

algorithm bandit constraints cs.cr cs.lg cs.sy decision differential privacy eess.sy hard high linear local math.oc mechanism parameter privacy private problem stat.ml under

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