面向隐私保护的随机量化器设计
Design of Stochastic Quantizers for Privacy Preservation
AI总结：
本文研究随机量化器的隐私保护作用，分析静态量化器的隐私保证与性能权衡，进一步提出动态随机量化器，可在同等控制性能下提升隐私水平，并通过高斯噪声处理不稳定场景。
摘要：
本文研究了随机量化器在隐私保护中的作用。我们首先采用静态随机量化器，研究其对应的隐私保护特性。具体而言，我们证明了足够大的量化步长可保证(0, δ)差分隐私。此外，我们将量化导致的控制性能下降评估为输出调节的跟踪误差。这两项分析刻画了由量化步长决定的隐私与控制性能之间的权衡关系。这一洞见使我们能够有意将量化作为一种手段，同时实现维持控制性能与保护隐私这两个看似矛盾的目标；为此，我们进一步研究了动态随机量化器。在稳定性假设下，动态随机量化器可在达到与静态随机量化器相同控制性能的同时，实现比后者更强的隐私保护效果。我们还通过额外施加输入高斯噪声，处理了不稳定场景下的相关问题。
英文摘要：
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 $(0, δ)$ 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.
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