DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms

Shweta Patwa, Danyu Sun, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy

Research output: Contribution to journalConference articlepeer-review

Abstract

Synthetic data generation methods, and in particular, private synthetic data generation methods, are gaining popularity as a means to make copies of sensitive databases that can be shared widely for research and data analysis. Some of the fundamental operations in data analysis include analyzing aggregated statistics, e.g., count, sum, or median, on a subset of data satisfying some conditions. When synthetic data is generated, users may be interested in knowing if their aggregated queries generating such statistics can be reliably answered on the synthetic data, for instance, to decide if the synthetic data is suitable for specific tasks. However, the standard data generation systems do not provide “per-query” quality guarantees on the synthetic data, and the users have no way of knowing how much the aggregated statistics on the synthetic data can be trusted. To address this problem, we present a novel framework named DP-PQD (differentially-private per-query decider) to detect if the query answers on the private and synthetic datasets are within a user-specified threshold of each other while guaranteeing differential privacy. We give a suite of private algorithms for per-query deciders for count, sum, and median queries, analyze their properties, and evaluate them experimentally.

Original languageEnglish
Pages (from-to)65-78
Number of pages14
JournalProceedings of the VLDB Endowment
Volume17
Issue number1
DOIs
StatePublished - 2023
Event50th International Conference on Very Large Data Bases, VLDB 2024 - Guangzhou, China
Duration: 24 Aug 202429 Aug 2024

Bibliographical note

Publisher Copyright:
© 2023, VLDB Endowment. All rights reserved.

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