Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy
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-point outputs. In addition, 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....
| Authors | Nikita P. Kalinin, Rasmus Pagh |
|---|---|
| Primary category | cs.CR |
| Published | 7 July 2026 |
| Last updated at source | 1 October 2026 |
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