Theory and Practice of Differential Privacy

Rachel Cummings · Gautam Kamath


Differential privacy is a promising approach to privacy-preserving data analysis. It has been the subject of a decade of intense scientific study, and has now been deployed in products at government agencies such as the U.S. Census Bureau and companies like Microsoft, Apple, and Google. MIT Technology Review named differential privacy one of 10 breakthrough technologies of 2020.
Since data privacy is a pervasive concern, differential privacy has been studied by researchers from many distinct communities, including machine learning, statistics, algorithms, computer security, cryptography, databases, data mining, programming languages, social sciences, and law. We believe that this combined effort across a broad spectrum of computer science is essential for differential privacy to realize its full potential. To this end, our workshop will stimulate discussion among participants about both the state-of-the-art in differential privacy and the future challenges that must be addressed to make differential privacy more practical.

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Timezone: America/Los_Angeles »