Numeric experiments show the improvement of the truncated Laplacian mechanism over the optimal Gaussian mechanism by significantly reducing the noise amplitude ...

The Laplace mechanism and the Gaussian mechanism are primary mechanisms in differential privacy, widely applicable to many sce- narios involving numerical data.

Nov 1, 2019 · We propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace ...

Abstract. The Laplace mechanism is the workhorse of differential privacy, applied to many instances where numerical data is processed.

Numeric experiments show the improve- ment of the truncated Laplacian mechanism over the optimal Gaussian mechanism in all privacy regimes. 1 Introduction.

In the next section we'll see a much more general feature of approximate DP, but let's start with a very simple example called the truncated Laplace mechanism.

Mar 31, 2022 · We propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace ...

Oct 1, 2018 · We derive a class of noise probability distributions to preserve (ϵ, δ)-differential privacy for single real-valued query function.

We demonstrate that the process of truncation and normalization of a Laplace PDF is not sufficient on its own to preserve the differential privacy guaran- tee. ...

Numeric experiments show the improvement of the truncated Laplacian mechanism over the optimal Gaussian mechanism by significantly reducing the noise amplitude ...