We study the problem of estimating finite sample confidence intervals of the mean of a normal population under the constraint of differential privacy. We consider both the known and unknown variance cases and construct differentially private algorithms to estimate confidence intervals. Crucially, our algorithms guarantee a finite sample coverage, as opposed to an asymptotic coverage. Unlike most previous differentially private algorithms, we do not require the domain of the samples to be bounded. We also prove lower bounds on the expected size of any differentially private confidence set showing that our the parameters are optimal up to polylogarithmic factors.

We present efficient algorithms for releasing useful statistics about graph data while providing rigorous privacy guarantees. Our algorithms work on datasets that consist of relationships between individuals, such as social ties or email communication. The algorithms satisfy edge differential privacy , which essentially requires that the presence or absence of any particular relationship be hidden. Our algorithms output approximate answers to subgraph counting queries . Given a query graph H , for example, a triangle, k -star, or k -triangle, the goal is to return the number of edge-induced isomorphic copies of H in the input graph. The special case of triangles was considered by Nissim et al. [2007] and a more general investigation of arbitrary query graphs was initiated by Rastogi et al. [2009]. We extend the approach of Nissim et al. to a new class of statistics, namely k -star queries. We also give algorithms for k -triangle queries using a different approach based on the higher-order local sensitivity. For the specific graph statistics we consider (i.e., k -stars and k -triangles), we significantly improve on the work of Rastogi et al.: our algorithms satisfy a stronger notion of privacy that does not rely on the adversary having a particular prior distribution on the data, and add less noise to the answers before releasing them. We evaluate the accuracy of our algorithms both theoretically and empirically, using a variety of real and synthetic datasets. We give explicit, simple conditions under which these algorithms add a small amount of noise. We also provide the average-case analysis in the Erdős-Rényi-Gilbert G ( n , p ) random graph model. Finally, we give hardness results indicating that the approach Nissim et al. used for triangles cannot easily be extended to k -triangles (hence justifying our development of a new algorithmic approach).