Optimizing fitness-for-use of differentially private linear Queries
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.
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Related Subject Headings
- 4605 Data management and data science
- 0807 Library and Information Studies
- 0806 Information Systems
- 0802 Computation Theory and Mathematics
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- 4605 Data management and data science
- 0807 Library and Information Studies
- 0806 Information Systems
- 0802 Computation Theory and Mathematics