Handbook of sharing confidential data : differential privacy, secure multiparty computation, and synthetic data

著者

    • Drechsler, Jörg

書誌事項

Handbook of sharing confidential data : differential privacy, secure multiparty computation, and synthetic data

edited by Jörg Drechsler, Daniel Kifer, Jerome Reiter and Aleksandra Slavkovic

(Chapman & Hall/CRC handbooks of modern statistical methods)

CRC Press, 2025

First edition

大学図書館所蔵 件 / 1

この図書・雑誌をさがす

注記

Includes bibliographical references and index.

Summary:"Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma. They need to protect the privacy and confidentiality of data subjects' and their attributes while providing data products that are useful for their intended purposes. In an age when information on data subjects is available from a wide range of data sources, as are the computational resources to obtain that information, this challenge is increasingly difficult. The Handbook of Sharing Confidential Data helps data stewards understand how tools from the data confidentiality literature-specifically, synthetic data, formal privacy, and secure computation-can be used to manage trade-offs in disclosure risk and data usefulness"-- Provided by publisher.

収録内容

  • Protecting confidential data through non-statistical methods / Lars Vilhuber.

詳細情報

ページトップへ