Sharing Big Data Safely: Managing Data Security

Ted Dunning, Ellen Friedman

Sharing Big Data Safely: Managing Data Security

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商品描述

Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away.

Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to:

  • Share original data in a controlled way so that different groups within your organization only see part of the whole. You’ll learn how to do this with the new open source SQL query engine Apache Drill.
  • Provide synthetic data that emulates the behavior of sensitive data. This approach enables external advisors to work with you on projects involving data that you can't show them.

If you’re intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. You’ll also get a collection of use cases.

Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, you’ll discover new options to share data safely without sacrificing security.

商品描述(中文翻譯)

如今，許多以大數據為基礎的公司正開始保護某些類型的數據，以防止入侵、洩漏或未經授權的查閱。但是，在授予需要查看數據的人員訪問權限的同時，如何鎖定數據呢？在這本實用的書中，作者Ted Dunning和Ellen Friedman提供了兩種創新且實用的解決方案，您可以立即實施。

這本書適合技術和非技術的決策者、團隊領導者、開發人員和數據科學家閱讀，它向您展示了如何：

- 以受控的方式共享原始數據，使組織內的不同團隊只能看到整體的一部分。您將學習如何使用新的開源SQL查詢引擎Apache Drill來實現這一點。
- 提供模擬敏感數據行為的合成數據。這種方法使得外部顧問可以在涉及您無法向他們展示的數據的項目上與您合作。

如果您對合成數據解決方案感興趣，可以探索Ted Dunning開發的開源代碼log-synth（在GitHub上可用），以及使用說明和最佳實踐的技巧。您還將獲得一系列使用案例。

在安全共享數據的同時提供鎖定安全性是越來越多組織面臨的重大挑戰。通過這本書，您將發現在不牺牲安全性的情況下安全共享數據的新選擇。

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