Skip to main content

Optimizing fitness-for-use of differentially private linear Queries

Publication ,  Journal Article
Xiao, Y; Ding, Z; Wang, Y; Zhang, D; Kifer, D
Published in: Proceedings of the VLDB Endowment
January 1, 2021

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.

Duke Scholars

Published In

Proceedings of the VLDB Endowment

DOI

EISSN

2150-8097

Publication Date

January 1, 2021

Volume

14

Issue

10

Start / End Page

1730 / 1742

Related Subject Headings

  • 4605 Data management and data science
  • 0807 Library and Information Studies
  • 0806 Information Systems
  • 0802 Computation Theory and Mathematics
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Xiao, Y., Ding, Z., Wang, Y., Zhang, D., & Kifer, D. (2021). Optimizing fitness-for-use of differentially private linear Queries. Proceedings of the VLDB Endowment, 14(10), 1730–1742. https://doi.org/10.14778/3467861.3467864
Xiao, Y., Z. Ding, Y. Wang, D. Zhang, and D. Kifer. “Optimizing fitness-for-use of differentially private linear Queries.” Proceedings of the VLDB Endowment 14, no. 10 (January 1, 2021): 1730–42. https://doi.org/10.14778/3467861.3467864.
Xiao Y, Ding Z, Wang Y, Zhang D, Kifer D. Optimizing fitness-for-use of differentially private linear Queries. Proceedings of the VLDB Endowment. 2021 Jan 1;14(10):1730–42.
Xiao, Y., et al. “Optimizing fitness-for-use of differentially private linear Queries.” Proceedings of the VLDB Endowment, vol. 14, no. 10, Jan. 2021, pp. 1730–42. Scopus, doi:10.14778/3467861.3467864.
Xiao Y, Ding Z, Wang Y, Zhang D, Kifer D. Optimizing fitness-for-use of differentially private linear Queries. Proceedings of the VLDB Endowment. 2021 Jan 1;14(10):1730–1742.

Published In

Proceedings of the VLDB Endowment

DOI

EISSN

2150-8097

Publication Date

January 1, 2021

Volume

14

Issue

10

Start / End Page

1730 / 1742

Related Subject Headings

  • 4605 Data management and data science
  • 0807 Library and Information Studies
  • 0806 Information Systems
  • 0802 Computation Theory and Mathematics