Название: Hands-On Differential Privacy: Introduction to the Theory and Practice Using OpenDP
Автор: Ethan Cowan, Michael Shoemate, Mayana Pereira
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 362
Язык: английский
Формат: True PDF, True/Retail EPUB
Размер: 12.6 MB

Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help.

Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows.

The OpenDP Library is part of the larger OpenDP Project. OpenDP is a community effort to build a trustworthy suite of open-source tools for enabling privacy-protective analysis of sensitive personal data, focused on a library of algorithms for generating differentially private statistical releases. The target use cases for OpenDP are to enable government, industry, and academic institutions to safely and confidently share sensitive data to support scientifically oriented research and exploration in the public interest. We aim for OpenDP to flexibly grow with the rapidly advancing science of differential privacy, and be a pathway to bring the newest algorithmic developments to a wide array of practitioners. The full documentation for OpenDP can be found at the OpenDP website.

SmartNoise is a collaboration between Microsoft, Harvard’s Institute for Quantitative Social Science (IQSS), and the OpenDP Project. The project aims to connect solutions from the research community with the lessons learned from real-world deployments to make differential privacy broadly accessible. Building upon the foundation of the OpenDP Library, the SmartNoise SDK includes two Python packages:

smartnoise-sql
Allows data owners to run differentially private SQL queries. This package is useful when generating reports or data cubes over tabular data stored in SQL databases or Spark, or when the data set is very large.

smartnoise-synth
Provides utilities for generating differentially private synthetic data sets. This is useful when you can’t predict the workload in advance and want to be able to share data that is structurally similar to the real data with collaborators.

Tumult Core is a set of components for building differentially private computations. Tumult Analytics is a Python library, built on top of Tumult Core, that allows the user to perform differentially private queries on tabular data. Tumult also has an online platform for both batch and interactive data analysis. In May 2023, Tumult and Google announced a strategic partnership integrating Tumult’s differential privacy techniques into Google’s BigQuery platform. This allows BigQuery users to make differentially private queries on the platform by adding a differential privacy clause to their query.

With this book, you'll learn:

How DP guarantees privacy when other data anonymization methods don't
What preserving individual privacy in a dataset entails
How to apply DP in several real-world scenarios and datasets
Potential privacy attack methods, including what it means to perform a reidentification attack
How to use the OpenDP library in privacy-preserving data releases
How to interpret guarantees provided by specific DP data releases




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