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Synthetic data generation methods, and in particular, private synthetic datageneration methods, are gaining popularity as a means to make copies ofsensitive databases that can be shared widely for research and data analysis.Some of the fundamental operations in data analysis include analyzingaggregated statistics, e.g., count, sum, or median, on a subset of datasatisfying some conditions. When synthetic data is generated, users may beinterested in knowing if their aggregated queries generating such statisticscan be reliably answered on the synthetic data, for instance, to decide if thesynthetic data is suitable for specific tasks. However, the standard datageneration systems do not provide "per-query" quality guarantees on thesynthetic data, and the users have no way of knowing how much the aggregatedstatistics on the synthetic data can be trusted. To address this problem, wepresent a novel framework named DP-PQD (differentially-private per-querydecider) to detect if the query answers on the private and synthetic datasetsare within a user-specified threshold of each other while guaranteeingdifferential privacy. We give a suite of private algorithms for per-querydeciders for count, sum, and median queries, analyze their properties, andevaluate them experimentally.

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