Four Facets of Differential Privacy
Differential privacy disentangles learning about a dataset as a whole from learning about an individual data contributor. Just now entering practice on a global scale, the demand for advanced differential privacy techniques and knowledge of basic skills is pressing. The Differential Privacy Symposium: Four Facets of Differential Privacy, held at the Institute for Advanced Study on November 12, 2016, provided an in-depth look at the current context for privacy-preserving statistical data analysis and an agenda for future research. This event was organized by Cynthia Dwork, of Microsoft Research, with support from the Alfred P. Sloan Foundation.
Introduction: The Definition of Differential Privacy by Cynthia Dwork, Microsoft Research
Composition: The Key to Differential Privacy’s Success by Guy Rothblum, Weizmann Institute of Science and former Visitor in the School of Mathematics
Differentially Private Algorithms: Some Primitives and Paradigms by Kunal Talwar, Google Brain
Differential Privacy in Context: Conceptual and Ethical Considerations by Helen Nissenbaum, Cornell Tech and New York University
Rigorous Data Dredging: Theory and Tools for Adaptive Data Analysis by Aaron Roth, The University of Pennsylvania