Database Anonymization: Privacy Models, Data Utility, and Microaggregation-based Inter-model Connections (Synthesis Lectures on Information Security, Privacy, and Trust)
Josep Domingo-Ferrer, David Sánchez, Jordi Soria-Comas
- 出版商: Morgan & Claypool
- 出版日期: 2016-01-01
- 售價: $1,720
- 貴賓價: 9.5 折 $1,634
- 語言: 英文
- 頁數: 138
- 裝訂: Paperback
- ISBN: 1627058435
- ISBN-13: 9781627058438
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相關分類:
資料庫、資訊安全
海外代購書籍(需單獨結帳)
相關主題
商品描述
The current social and economic context increasingly demands open data to improve scientific research and decision making. However, when published data refer to individual respondents, disclosure risk limitation techniques must be implemented to anonymize the data and guarantee by design the fundamental right to privacy of the subjects the data refer to. Disclosure risk limitation has a long record in the statistical and computer science research communities, who have developed a variety of privacy-preserving solutions for data releases. This Synthesis Lecture provides a comprehensive overview of the fundamentals of privacy in data releases focusing on the computer science perspective. Specifically, we detail the privacy models, anonymization methods, and utility and risk metrics that have been proposed so far in the literature. Besides, as a more advanced topic, we identify and discuss in detail connections between several privacy models (i.e., how to accumulate the privacy guarantees they offer to achieve more robust protection and when such guarantees are equivalent or complementary); we also explore the links between anonymization methods and privacy models (how anonymization methods can be used to enforce privacy models and thereby offer ex ante privacy guarantees). These latter topics are relevant to researchers and advanced practitioners, who will gain a deeper understanding on the available data anonymization solutions and the privacy guarantees they can offer.
商品描述(中文翻譯)
當前的社會和經濟背景越來越需要開放數據,以改善科學研究和決策。然而,當發佈的數據涉及個別受訪者時,必須實施披露風險限制技術,以匿名化數據並設計上保證數據所涉及主體的基本隱私權。披露風險限制在統計學和計算機科學研究社群中有著悠久的歷史,這些社群已經開發出多種保護隱私的數據發佈解決方案。本次綜合講座提供了關於數據發佈隱私基本原則的全面概述,重點關注計算機科學的視角。具體而言,我們詳細介紹了文獻中提出的隱私模型、匿名化方法以及效用和風險指標。此外,作為一個更高級的主題,我們識別並詳細討論了幾個隱私模型之間的聯繫(即如何累積它們所提供的隱私保證以實現更強的保護,以及何時這些保證是等價或互補的);我們還探討了匿名化方法與隱私模型之間的聯繫(即如何使用匿名化方法來強制執行隱私模型,從而提供事前的隱私保證)。這些後者主題對於研究人員和高級從業者來說是相關的,因為他們將對可用的數據匿名化解決方案及其所能提供的隱私保證有更深入的理解。