Design of Stochastic Quantizers for Privacy Preservation
<|reference_start|>Design of Stochastic Quantizers for Privacy Preservation: In this paper, we examine the role of stochastic quantizers for privacy preservation. We first employ a static stochastic quantizer and investigate its corresponding privacy-preserving properties. Specifically, we demonstrate that a sufficiently large quantization step guarantees $(0, \delta)$ differential privacy. Additionally, the degradation of control performance caused by quantization is evaluated as the tracking error of output regulation. These two analyses characterize the trade-off between privacy and control performance, determined by the quantization step. This insight enables us to use quantization intentionally as a means to achieve the seemingly conflicting two goals of maintaining control performance and preserving privacy at the same time; towards this end, we further investigate a dynamic stochastic quantizer. Under a stability assumption, the dynamic stochastic quantizer can enhance privacy, more than the static one, while achieving the same control performance. We further handle the unstable case by additionally applying input Gaussian noise.<|reference_end|>
@article{liu2024design,
    title={Design of Stochastic Quantizers for Privacy Preservation},
    author={Le Liu, Yu Kawano, Ming Cao},
    journal={arXiv preprint arXiv:2403.03048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03048},
    primaryClass={eess.SY cs.CR cs.SY}
}