Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges

By Ashwin Machanavajjhala, Xi He, Michael Hay. 2016

56 citations recorded by the source corpus.

Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security, database, networks, machine learning, and statistics, over the last decade has resulted in a variety of privacy preserving algorithms for a number of analysis tasks. Despite maturing research efforts, the adoption of differential privacy by practitioners in industry, academia, or government agencies has so far been rare. Hence, in this tutorial, we will first describe the foundations of differentially private algorithm design that cover the state of the art in private computation on tabular data. In the second half of the tutorial we will highlight real world applications on complex data types, and identify research challenges in applying differential privacy to real world applications.

Subfields: Artificial Intelligence, Sociology and Political Science

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Citations in the corpus

Marginal Release Under Local Differential Privacy (2017, background): “It has been a topic of inquiry for diverse research communities including theory [35], data management [25], machine learning [33], and systems/programming languages [27].”
Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “The actual magnitude of added noise directly depends upon the global sensitivity and privacy budget [87].”