  Since its introduction in 2006, differential privacy has emerged as a
predominant statistical tool for quantifying data privacy in academic works.
Yet despite the plethora of research and open-source utilities that have
accompanied its rise, with limited exceptions, differential privacy has failed
to achieve widespread adoption in the enterprise domain. Our study aims to shed
light on the fundamental causes underlying this academic-industrial utilization
gap through detailed interviews of 24 privacy practitioners across 9 major
companies. We analyze the results of our survey to provide key findings and
suggestions for companies striving to improve privacy protection in their data
workflows and highlight the necessary and missing requirements of existing
differential privacy tools, with the goal of guiding researchers working
towards the broader adoption of differential privacy. Our findings indicate
that analysts suffer from lengthy bureaucratic processes for requesting access
to sensitive data, yet once granted, only scarcely-enforced privacy policies
stand between rogue practitioners and misuse of private information. We thus
argue that differential privacy can significantly improve the processes of
requesting and conducting data exploration across silos, and conclude that with
a few of the improvements suggested herein, the practical use of differential
privacy across the enterprise is within striking distance.
