In this paper, we revisit the problem of Differentially Private Stochastic
Convex Optimization (DP-SCO) and provide excess population risks for some
special classes of functions that are faster than the previous results of
general convex and strongly convex functions. In the first part of the paper,
we study the case where the population risk function satisfies the Tysbakov
Noise Condition (TNC) with some parameter $theta>1$. Specifically, we first
show that under some mild assumptions on the loss functions, there is an
algorithm whose output could achieve an upper bound of
frac{1}{delta}}}{nepsilon})^frac{theta}{theta-1})$ for $(epsilon,
delta)$-DP when $thetageq 2$, here $n$ is the sample size and $d$ is the
dimension of the space. Then we address the inefficiency issue, improve the
upper bounds by $text{Poly}(log n)$ factors and extend to the case where
$thetageq bar{theta}>1$ for some known $bar{theta}$. Next we show that
the excess population risk of population functions satisfying TNC with
parameter $theta>1$ is always lower bounded by
$Omega((frac{d}{nepsilon})^frac{theta}{theta-1}) $ and
frac{1}{delta}}}{nepsilon})^frac{theta}{theta-1})$ for $epsilon$-DP and
$(epsilon, delta)$-DP, respectively. In the second part, we focus on a
special case where the population risk function is strongly convex. Unlike the
previous studies, here we assume the loss function is {em non-negative} and
{em the optimal value of population risk is sufficiently small}. With these
additional assumptions, we propose a new method whose output could achieve an
upper bound of
$O(frac{dlogfrac{1}{delta}}{n^2epsilon^2}+frac{1}{n^{tau}})$ for any
$taugeq 1$ in $(epsilon,delta)$-DP model if the sample size $n$ is
sufficiently large.

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