Guilt-Free Interactive Data Analysis

A great deal of effort has been devoted to reducing the risk of spurious scientific discoveries, from the use of holdout sets and sophisticated cross-validation techniques, to procedures for controlling the false discovery rate in multiple hypothesis testing. However, there is a fundamental disconnect between the theoretical results and the practice of science: the theory assumes a fixed collection of hypotheses to be tested, or learning algorithms to be applied, selected non-adaptively before the data are gathered, whereas science is by definition an adaptive process, in which data are shared and reused, and hypotheses and new studies are generated on the basis of data exploration and previous outcomes.

Surprisingly, the challenges of adaptivity can be addressed using insights from differential privacy, a field of study supporting a definition of privacy tailored to private data analysis. As a corollary we show how to safely reuse a holdout set a great many times without undermining its power of “correctness protection,” even when hypotheses and computations are chosen adaptively. Armed with this technique, the analyst is free to explore the data ad libitum, generating and evaluating hypotheses, verifying results on the holdout, and backtracking as needed.

Joint work with Cynthia Dwork, Vitaly Feldman, Toni Pitassi, Omer Reingold and Aaron Roth.