Explaining Differentially Private Query Results With DPXPlain

Tingyu Wang, Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)3962-3965
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - 2023
Externally publishedYes
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: 28 Aug 20231 Sep 2023


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