The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy

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Cynthia Dwork
Jonathan Ullman

Abstract

Differential Privacy is a popular technology for privacy-preserving analysis of large datasets. DP is powerful, but it requires that the analyst interact with data only through a special interface; in particular, the analyst does not see raw data, an uncomfortable situation for anyone trained in classical statistical data analysis. In this note we discuss the (overly) simple problem of allowing a  trusted analyst to choose an ``"interesting" statistic for popular release (the actual computation of the chosen statistic will be carried out in a differentially private way).

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How to Cite
Dwork, Cynthia, and Jonathan Ullman. 2018. “The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy”. Journal of Privacy and Confidentiality 8 (1). https://doi.org/10.29012/jpc.687.
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