Journal
PROCEEDINGS OF THE VLDB ENDOWMENT
Volume 9, Issue 13, Pages 1611-1614Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.14778/3007263.3007322
Keywords
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Funding
- Division Of Computer and Network Systems
- Direct For Computer & Info Scie & Enginr [1253327] Funding Source: National Science Foundation
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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.
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