Practical Data Privacy: Enhancing Privacy and Security in Data

Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure for data scientists to ensure data privacy. Unfortunately, integrating privacy into your data science workflow is still complicated. This essential guide will give you solid advice and best practices on breakthrough privacy-enhancing technologies such as encrypted learning and differential privacy--as well as a look at emerging technologies and techniques in the field.

Practical Data Privacy answers important questions such as:

• What do privacy regulations like GDPR and CCPA mean for my project?
• What does "anonymized data" really mean?
• Should I anonymize the data? If so, how?
• Which privacy techniques fit my project and how do I incorporate them?
• What are the differences and similarities between privacy-preserving technologies and methods?
• How do I utilize an open-source library for a privacy-enhancing technique?
• How do I ensure that my projects are secure by default and private by design?
• How do I create a plan for internal policies or a specific data project that incorporates privacy and security from the start?

在像GDPR和CCPA這樣的重要隱私法規和昂貴且臭名昭著的數據洩露事件之間，數據科學家面臨著前所未有的數據隱私壓力。不幸的是，將隱私整合到數據科學工作流程中仍然很複雜。這本重要指南將為您提供關於突破性的隱私增強技術（如加密學習和差分隱私）的實用建議和最佳實踐，以及對該領域新興技術和技巧的一瞥。

《實用數據隱私》回答了一些重要問題，例如：
- GDPR和CCPA等隱私法規對我的項目意味著什麼？
- 「匿名數據」到底是什麼意思？
- 我應該對數據進行匿名處理嗎？如果是，該如何進行？
- 哪些隱私技術適用於我的項目，我該如何將它們納入其中？
- 隱私保護技術和方法之間有什麼區別和相似之處？
- 如何使用開源庫進行隱私增強技術？
- 如何確保我的項目在設計時具有默認安全性和隱私性？
- 如何為內部政策或特定數據項目創建一個從一開始就納入隱私和安全的計劃？