Chapter 7 The Complexity of Differential Privacy Salil Vadhan Abstract Differential privacy is a theoretical framework for ensuring the privacy of individual-level data when performing statistical analysis of privacy-sensitive datasets. This tutorial provides an introduction to and overview of differential pri- vacy, with the goal of conveying its deep connections to a variety of other topics in computational complexity, cryptography, and theoretical computer science at large. This tutorial is written in celebration of Oded Goldreich’s 60th birthday, starting from notes taken during a minicourse given by the author and Kunal Talwar at the 26th McGill Invitational Workshop on Computational Complexity [1]. To Oded, my mentor, role model, collaborator, and friend. Your example gives me a sense of purpose as a researcher. Salil Vadhan Center for Research on Computation & Society, School of Engineering & Applied Sciences, Har- vard University, Cambridge, Massachusetts, USA, e-mail:
[email protected]. webpage: http://seas.harvard.edu/~salil. Written in part while visiting the Shing-Tung Yau Center and the Department of Applied Mathematics at National Chiao-Tung University in Hsinchu, Tai- wan. Supported by NSF grant CNS-1237235, a grant from the Sloan Foundation, and a Simons Investigator Award. © Springer International Publishing AG 2017 347 Y. Lindell (ed.), Tutorials on the Foundations of Cryptography, Information Security and Cryptography, DOI 10.1007/978-3-319-57048-8_7 348 Salil Vadhan 7.1 Introduction and Definition 7.1.1 Motivation Suppose you are a researcher in the health or social sciences who has collected a rich dataset on the subjects you have studied, and want to make the data available to others to analyze as well.