  In practice, differentially private data releases are designed to support a
variety of applications. A data release is fit for use if it meets target
accuracy requirements for each application. In this paper, we consider the
problem of answering linear queries under differential privacy subject to
per-query accuracy constraints. Existing practical frameworks like the matrix
mechanism do not provide such fine-grained control (they optimize total error,
which allows some query answers to be more accurate than necessary, at the
expense of other queries that become no longer useful). Thus, we design a
fitness-for-use strategy that adds privacy-preserving Gaussian noise to query
answers. The covariance structure of the noise is optimized to meet the
fine-grained accuracy requirements while minimizing the cost to privacy.
