这篇论文提出了一种更实用的差分隐私机制，在DP-SGD中能用更少的真随机数实现同等隐私保护，对关注隐私保护的开发者很有价值。
本文提出了一种名为Dithered Gaussian Mechanism的新机制，作为离散高斯机制的替代方案。该机制通过对高斯机制的输出进行后处理离散化，继承了标准高斯机制的隐私保证，并避免了浮点数精度漏洞。理论证明，该机制能显著减少高质量随机位的需求，并使其独立于噪声水平。在DP-SGD的应用中，该机制实现了加密级随机性生成，且实际开销可控。
Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy
We present the dithered Gaussian mechanism, a novel alternative to the discrete Gaussian mechanism for differential privacy that discretizes the private output rather than the noise distribution itself. By interpreting this discretization as post-processing of the Gaussian mechanism, our construction directly inherits the privacy guarantees of the standard Gaussian mechanism while avoiding vulnerabilities caused by finite-precision floating-point outputs. We show that the mechanism is provably randomness-efficient: by sampling the discretized output values directly, the number of high-quality random bits required for privacy can be reduced significantly and made independent of the noise level. This is achieved by separating the randomness into two sources: a high-quality source used for the privacy-critical sampling step, and a high-performance public source, possibly known to the adversary, that supplies the additional randomness needed for randomized discretization. This separation enables the use of cryptographically secure randomness without substantial performance loss. As an application, we study model training with DP-SGD and show that cryptographically secure noise generation with reduced exposure to floating-point vulnerabilities can be achieved with modest practical overhead.