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Explaining Differentially Private Query Results With DPXPlain

Publication ,  Conference
Wang, T; Tao, Y; Gilad, A; Machanavajjhala, A; Roy, S
Published in: Proceedings of the VLDB Endowment
January 1, 2023
Published version (DOI)

Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately ‘top-k’ explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.

Duke Scholars

Sudeepa Roy
Author Sudeepa Roy Computer Science
Ashwinkumar Venkatanaga Machanavajjhala
Author Ashwinkumar Venkatanaga Machanavajjhala Computer Science

Published In

Proceedings of the VLDB Endowment

DOI

10.14778/3611540.3611596

EISSN

2150-8097

Publication Date

January 1, 2023

Volume

16

Issue

12

Start / End Page

3962 / 3965

Related Subject Headings

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

Citation

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Wang, T., Tao, Y., Gilad, A., Machanavajjhala, A., & Roy, S. (2023). Explaining Differentially Private Query Results With DPXPlain. In Proceedings of the VLDB Endowment (Vol. 16, pp. 3962–3965). https://doi.org/10.14778/3611540.3611596
Wang, T., Y. Tao, A. Gilad, A. Machanavajjhala, and S. Roy. “Explaining Differentially Private Query Results With DPXPlain.” In Proceedings of the VLDB Endowment, 16:3962–65, 2023. https://doi.org/10.14778/3611540.3611596.
Wang T, Tao Y, Gilad A, Machanavajjhala A, Roy S. Explaining Differentially Private Query Results With DPXPlain. In: Proceedings of the VLDB Endowment. 2023. p. 3962–5.
Wang, T., et al. “Explaining Differentially Private Query Results With DPXPlain.” Proceedings of the VLDB Endowment, vol. 16, no. 12, 2023, pp. 3962–65. Scopus, doi:10.14778/3611540.3611596.
Wang T, Tao Y, Gilad A, Machanavajjhala A, Roy S. Explaining Differentially Private Query Results With DPXPlain. Proceedings of the VLDB Endowment. 2023. p. 3962–3965.

Published In

Proceedings of the VLDB Endowment

DOI

10.14778/3611540.3611596

EISSN

2150-8097

Publication Date

January 1, 2023

Volume

16

Issue

12

Start / End Page

3962 / 3965

Related Subject Headings

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