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

Cowan, Ethan, Shoemate, Michael, Pereira, Mayana

  • 出版商: O'Reilly
  • 出版日期: 2024-06-25
  • 售價: $2,720
  • 貴賓價: 9.5$2,584
  • 語言: 英文
  • 頁數: 360
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1492097748
  • ISBN-13: 9781492097747
  • 相關分類: 資訊安全
  • 立即出貨 (庫存=1)

商品描述

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 and 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.

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

商品描述(中文翻譯)

現今許多組織都在分析和分享關於個人的大型敏感數據集。無論這些數據集涵蓋醫療細節、財務記錄還是考試成績,組織要通過去識別化、匿名化和其他傳統的統計披露限制技術來保護個人信息變得更加困難。本實用書籍解釋了差分隱私(DP)如何幫助解決這個問題。

作者Ethan Cowan、Michael Shoemate和Mayana Pereira解釋了這些技術如何使數據科學家、研究人員和程序員能夠運行統計分析,隱藏任何單個個體的貢獻。您將深入了解基本的差分隱私概念,並了解如何使用開源工具創建差分隱私統計數據,探索如何評估效用/隱私權衡,以及學習如何將差分隱私整合到工作流程中。

通過本書,您將學到:
- 在其他數據匿名化方法無法保證隱私時,差分隱私如何保證隱私
- 在數據集中保護個人隱私的內涵
- 如何在幾個實際場景和數據集中應用差分隱私
- 潛在的隱私攻擊方法,包括進行重新識別攻擊的含義
- 如何在保護隱私的數據發布中使用OpenDP庫
- 如何解釋特定差分隱私數據發布所提供的保證