Differentially Private High Dimensional Bandits
<|reference_start|>Differentially Private High Dimensional Bandits: 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 for PrivateLASSO for the central model under standard assumptions.<|reference_end|>
@article{shukla2024differentially,
    title={Differentially Private High Dimensional Bandits},
    author={Apurv Shukla},
    journal={arXiv preprint arXiv:2402.03737},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03737},
    primaryClass={cs.LG cs.CR cs.SY eess.SY math.OC stat.ML}
}