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.

翻译：在实践中,有差别的私人数据发布旨在支持各种应用。如果数据发布符合每项应用的目标准确性要求,则适合使用数据发布。在本文中,我们考虑在受每个询问的准确性限制的情况下,在不同的隐私下回答线性询问的问题。像矩阵机制这样的现有实用框架并不提供这种细微控制(它们优化了总错误,使得某些查询回答比必要更准确,而忽略了不再有用的其他查询 ) 。因此,我们设计了一种适合使用的策略,在查询答案中增加隐私保护高斯噪音。噪音的替代结构优化了,以满足细微准确性要求,同时尽量减少隐私的成本。