Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy

Excerpt

arXiv:2607.06320v3 Announce Type: replace-cross Abstract: We present the dithered Gaussian mechanism, an 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-po