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Publisher ‏ : ‎ O’Reilly Media; 1st edition (June 25, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 360 pages
ISBN-10 ‏ : ‎ 1492097748
ISBN-13 ‏ : ‎ 978-1492097747
Item Weight ‏ : ‎ 1.27 pounds
Dimensions ‏ : ‎ 7 x 0.75 x 9.19 inches

Hands-On Differential Privacy: Introduction to the Theory and Practice Using OpenDP

Differential privacy has become a crucial concept in the field of data privacy, ensuring that sensitive information about individuals is protected while still allowing for meaningful analysis to be conducted. In this post, we will explore the theory and practice of differential privacy, using the OpenDP library to implement and test differentially private algorithms.

OpenDP is an open-source library that provides tools for implementing differential privacy in various data analysis tasks. By using OpenDP, users can easily apply differential privacy techniques to their data without having to worry about the details of the underlying algorithms.

In this hands-on guide, we will cover the basics of differential privacy, including the definition of epsilon-differential privacy and how it can be achieved through noise addition mechanisms. We will then walk through a series of examples using OpenDP to implement differentially private algorithms for tasks such as counting queries, mean estimation, and histogram computation.

By the end of this post, you will have a solid understanding of the theory behind differential privacy and practical experience using OpenDP to apply these concepts to real-world data analysis tasks. Whether you are a data scientist, privacy researcher, or simply curious about how differential privacy works, this post will provide you with the knowledge and tools to get started.
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