The Gaussian distribution is widely used in mechanism design for differential
privacy (DP). Thanks to its sub-Gaussian tail, it significantly reduces the
chance of outliers when responding to queries. However, it can only provide
approximate $(epsilon, delta(epsilon))$-DP. In practice, $delta(epsilon)$
must be much smaller than the size of the dataset, which may limit the use of
the Gaussian mechanism for large datasets with strong privacy requirements. In
this paper, we introduce and analyze a new distribution for use in DP that is
based on the Gaussian distribution, but has improved privacy performance. The
so-called offset-symmetric Gaussian tail (OSGT) distribution is obtained
through using the normalized tails of two symmetric Gaussians around zero.
Consequently, it can still have sub-Gaussian tail and lend itself to analytical
derivations. We analytically derive the variance of the OSGT random variable
and the $delta(epsilon)$ of the OSGT mechanism. We then numerically show that
at the same variance, the OSGT mechanism can offer a lower $delta(epsilon)$
than the Gaussian mechanism. We extend the OSGT mechanism to $k$-dimensional
queries and derive an easy-to-compute analytical upper bound for its
zero-concentrated differential privacy (zCDP) performance. We analytically
prove that at the same variance, the same global query sensitivity and for
sufficiently large concentration orders $alpha$, the OSGT mechanism performs
better than the Gaussian mechanism in terms of zCDP.

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