Full Citation

Title: Explaining Differentially Private Query Results with DPXPlain

Citation Type: Journal Article

Publication Year: 2023

ISBN:

ISSN: 2150-8097

DOI: 10.14778/3611540.3611596

NSFID:

PMCID:

PMID:

Abstract: 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.

Url: https://dl-acm-org.ezp2.lib.umn.edu/doi/10.14778/3611540.3611596

User Submitted?: No

Authors: WangTingyu, ; TaoYuchao, ; GiladAmir, ; MachanavajjhalaAshwin, ; RoySudeepa,

Periodical (Full): Proceedings of the VLDB Endowment

Issue: 12

Volume: 16

Pages: 3962-3965

Data Collections: IPUMS CPS

Topics: Methodology and Data Collection

Countries: