  In this work, we study trade-offs between accuracy and privacy in the context
of linear queries over histograms. This is a rich class of queries that
includes contingency tables and range queries, and has been a focus of a long
line of work. For a set of $d$ linear queries over a database $x \in \R^N$, we
seek to find the differentially private mechanism that has the minimum mean
squared error. For pure differential privacy, an $O(\log^2 d)$ approximation to
the optimal mechanism is known. Our first contribution is to give an $O(\log^2
d)$ approximation guarantee for the case of $(\eps,\delta)$-differential
privacy. Our mechanism is simple, efficient and adds correlated Gaussian noise
to the answers. We prove its approximation guarantee relative to the hereditary
discrepancy lower bound of Muthukrishnan and Nikolov, using tools from convex
geometry.
  We next consider this question in the case when the number of queries exceeds
the number of individuals in the database, i.e. when $d > n \triangleq
\|x\|_1$. It is known that better mechanisms exist in this setting. Our second
main contribution is to give an $(\eps,\delta)$-differentially private
mechanism which is optimal up to a $\polylog(d,N)$ factor for any given query
set $A$ and any given upper bound $n$ on $\|x\|_1$. This approximation is
achieved by coupling the Gaussian noise addition approach with a linear
regression step. We give an analogous result for the $\eps$-differential
privacy setting. We also improve on the mean squared error upper bound for
answering counting queries on a database of size $n$ by Blum, Ligett, and Roth,
and match the lower bound implied by the work of Dinur and Nissim up to
logarithmic factors.
  The connection between hereditary discrepancy and the privacy mechanism
enables us to derive the first polylogarithmic approximation to the hereditary
discrepancy of a matrix $A$.

Data Structures and Algorithms