  Organizations are increasingly interested in allowing external data
scientists to explore their sensitive datasets. Due to the popularity of
differential privacy, data owners want the data exploration to ensure provable
privacy guarantees. However, current systems for answering queries with
differential privacy place an inordinate burden on the data analysts to
understand differential privacy, manage their privacy budget, and even
implement new algorithms for noisy query answering. Moreover, current systems
do not provide any guarantees to the data analyst on the quality they care
about, namely accuracy of query answers.
  We present APEx, a novel system that allows data analysts to pose adaptively
chosen sequences of queries along with required accuracy bounds. By translating
queries and accuracy bounds into differentially private algorithms with the
least privacy loss, APEx returns query answers to the data analyst that meet
the accuracy bounds, and proves to the data owner that the entire data
exploration process is differentially private. Our comprehensive experimental
study on real datasets demonstrates that APEx can answer a variety of queries
accurately with moderate to small privacy loss, and can support data
exploration for entity resolution with high accuracy under reasonable privacy
settings.
