I have been doing research on data privacy and de-identification of personal health information for several years. This is the most comprehensive book on this subject I have ever had. When I ordered the book I was thinking that it would be a bit more like the academic style because of some technical aspects of the de-identification. Each section includes many references to academic papers or other resources, however, I have found that this book is very easy to read through and quite nicely flows; it is beautifully organized and written amazingly clear. Initially, I was targeting several sections, and planning to keep it as a reference book. Once I read several parts of the book, decided to go from cover to cover.
Don't confuse that this book is not a review of privacy literature but more of a walk-through guidance book on health data privacy. It provides an in-depth understanding of the need for de-identifying personal data, and essential privacy disclosure risks. It provides a detailed explanation on how to evaluate privacy risk, then, presents practical methods to de-identify personal data. Yes, it also discusses policies, regulations, and laws but it moves beyond philosophies and goes into the real mechanics, tools, and techniques without overwhelming the reader.
The discussions and examples in the book are mostly around health data privacy, however, many principles and method can be easily applied or adapted to other data types as well. I would say this book should be most helpful to the professional privacy practitioners, however, I think that it is a must-read resource for anyone who wants to have a deeper understanding of privacy risks on health data and the methods working in practice.
The only issue about my purchase is that the copy I have received has a defect on the cover. I did not mind it so much because I am much more interested in the content than the make-up. Overall, it is a great value!
![“Guide to the De-Identification of Personal Health Information (English Edition)”，作者：[Khaled El Emam]](https://images-cn.ssl-images-amazon.cn/images/I/41mhTryWDqL._SY346_.jpg)
Guide to the De-Identification of Personal Health Information (English Edition) 1第一 版本, Kindle电子书
广告
Offering compelling practical and legal reasons why de-identification should be one of the main approaches to protecting patients’ privacy, the Guide to the De-Identification of Personal Health Information outlines a proven, risk-based methodology for the de-identification of sensitive health information. It situates and contextualizes this risk-based methodology and provides a general overview of its steps.
The book supplies a detailed case for why de-identification is important as well as best practices to help you pin point when it is necessary to apply de-identification in the disclosure of personal health information. It also:
- Outlines practical methods for de-identification
- Describes how to measure re-identification risk
- Explains how to reduce the risk of re-identification
- Includes proofs and supporting reference material
- Focuses only on transformations proven to work on health information—rather than covering all possible approaches, whether they work in practice or not
Rated the top systems and software engineering scholar worldwide by The Journal of Systems and Software, Dr. El Emam is one of only a handful of individuals worldwide qualified to de-identify personal health information for secondary use under the HIPAA Privacy Rule Statistical Standard. In this book Dr. El Emam explains how we can make health data more accessible—while protecting patients’ privacy and complying with current regulations.
商品描述
媒体推荐
By arguing persuasively for the use of de-identification as a privacy-enhancing tool, and setting out a practical methodology for the use of de-identification techniques and re-identification risk measurement tools, this book provides a valuable and much needed resource for all data custodians who use or disclose personal health information for secondary purposes. Doubly enabling, privacy-enhancing tools like these, that embrace privacy by design, will ensure the continued availability of personal health information for valuable secondary purposes that benefit us all. -Dr. Ann Cavoukian, Information and Privacy Commissioner, Ontario, Canada --此文字指其他 kindle_edition 版本。
作者简介
Dr. El Emam holds the Canada Research Chair in Electronic Health Information at the University of Ottawa and is an Associate Professor in the Faculty of Medicine at the university. In 2003 and 2004, he was ranked as the top systems and software engineering scholar worldwide by The Journal of Systems and Software based on his research on measurement and quality evaluation and improvement. He is a senior scientist at the Children's Hospital of Eastern Ontario Research Institute and leads the multi-disciplinary Electronic Health Information Laboratory (EHIL) team. Dr. El Emam is one of only a handful of individuals worldwide known to be qualified to de-identify personal health information for secondary use under the HIPAA Privacy Rule Statistical Standard. Khaled is also a world-renowned expert in health information privacy and the head of the Electronic Health Information Laboratory www.ehealthinformation.ca which conducts cutting edge research in this area. He has been de-identifying data since 2004, and has a large following and speaks extensively on this topic. He has edited 2 books and written one already, as well as contributed chapters to a number of others. --此文字指其他 kindle_edition 版本。
目录
Introduction Primary and Secondary Purposes The Spectrum of Risk for Data Access Managing Risk What Is De-identification? Learning Something New The Status Quo Safe Harbor-Compliant Data Can Have a High Risk of Re-identification The Adversary Knows Who Is in the Data The Data Set Is Not a Random Sample from the U.S. Population Other Fields Can Be Used for Re-identification Moving Forward beyond Safe Harbor Why We Wrote This Book References THE CASE FOR DE-IDENTIFYING PERSONAL HEALTH INFORMATION Permitted Disclosures, Consent, and De-identification of PHI Common Data Flows The Need for De-identification Permitted Uses and Disclosures of Health Information Uses of Health Information by an Agent Disclosing Identifiable Data When Permitted References The Impact of Consent Differences between Consenters and Non-Consenters in Clinical Trials The Impact of Consent on Observational Studies Impact on Recruitment Impact on Bias Impact on Cost Impact on Time References Data Breach Notifications Benefits and Costs of Breach Notification Cost of Data Breach Notifications to Custodian Data Breach Trends The Value of Health Data Financial Information in the Health Records Financial Value of Health Records Medical Identity Theft Monetizing Health Records through Extortion References Peeping and Snooping Examples of Peeping Information and Privacy Commissioners Orders Ontario HO-002 HO-010 HR06-53 HI-050013-1 Alberta Investigation Report H2011-IR-004 IPC Investigation (Report Not Available) Saskatchewan H-2010-001 References Unplanned but Legitimate Uses and Disclosures Unplanned Uses by Governments Data Sharing for Research Purposes Open Government Open Data for Research Unplanned Uses and Disclosures by Commercial Players Competitions References Public Perception and Privacy Protective Behaviors References Alternative Methods for Data Access Remote Access On-Site Access Remote Execution Remote Queries Secure Computation Summary References UNDERSTANDING DISCLOSURE RISKS Scope, Terminology, and Definitions Perspective on De-identification Original Data and DFs Unit of Analysis Types of Data Relational Data Transactional Data Sequential Data Trajectory Data Graph Data The Notion of an Adversary Types of Variables Directly Identifying Variables Indirectly Identifying Variables (Quasi-identifiers) Sensitive Variables Other Variables Equivalence Classes Aggregate Tables References Frequently Asked Questions about De-identification Can We Have Zero Risk? Will All DFs Be Re-identified in the Future? Is a Data Set Identifiable If a Person Can Find His or Her Record? Can De-identified Data Be Linked to Other Data Sets? Doesn't Differential Privacy Already Provide the Answer? A Methodology for Managing Re-identification Risk Re-identification Risk versus Re-identification Probability Re-identification Risk for Public Files Managing Re-identification Risk References Definitions of Identifiability Definitions Common Framework for Assessing Identifiability References Data Masking Methods Suppression Randomization Irreversible Coding Reversible Coding Reversible Coding, HIPAA, and the Common Rule Other Techniques That Do Not Work Well Constraining Names Adding Noise Character Scrambling Character Masking Truncation Encoding Summary References Theoretical Re-identification Attacks Background Knowledge of the Adversary Re-identification Attacks Example of a Linking Attack on Relational Data Example of a Linking Attack on Transaction Data Example of a Linking Attack on Sequential Data Example of a Linking Attack on Trajectory Data Example of a Linking Attack Based on Semantic Information References MEASURING RE-IDENTIFICATION RISK Measuring the Probability of Re-identification Simple and Derived Metrics Simple Risk Metrics: Prosecutor and Journalist Risk Measuring Prosecutor Risk Measuring Journalist Risk Applying the Derived Metrics and Decision Rules Relationship among Metrics References Measures of Uniqueness Uniqueness under Prosecutor Risk Uniqueness under Journalist Risk Summary References Modeling the Threat Characterizing the Adversaries Attempting a Re-identification Attack Plausible Adversaries An Internal Adversary An External Adversary What Are the Quasi-identifiers? Sources of Data Correlated and Inferred Variables References Choosing Metric Thresholds Choosing the Threshold Choosing the and Thresholds Choosing the Threshold for Marketer Risk Choosing among Thresholds Thresholds and Incorrect Re-identification References PRACTICAL METHODS FOR DE-IDENTIFICATION De-identification Methods Generalization Principles Optimal Lattice Anonymization (OLA) Tagging Records to Suppress Suppression Methods Overview Fast Local Cell Suppression Available Tools Case Study: De-identification of the BORN Registry General Parameters Attack T1 Attack T2 Attack T3 Summary of Risk Assessment and De-identification References Practical Tips Disclosed Files Should Be Samples Disclosing Multiple Samples Creating Cohorts Cohort Defined on Quasi-identifiers Only Cohort Defined on a Non-Quasi-identifier Cohort Defined on Non-Quasi-identifiers and Quasi-identifiers Impact of Data Quality Publicizing Re-identification Risk Assessment Adversary Power Levels of Adversary Background Knowledge De-identification in the Context of a Data Warehouse References END MATTER An Analysis of Historical Breach Notification Trends Methods Definitions Breach Lists Original Data Sources Sponsors of Lists Data Quality Estimating the Number of Disclosed Breaches Data Collection Interrater Agreement Results Discussion Summary of Main Results Post Hoc Analysis References Methods of Attack for Maximum Journalist Risk Method of Attack 1 Method of Attack 2 Method of Attack 3 How Many Friends Do We Have? References Cell Size Precedents References The Invasion of Privacy Construct 6B Dimensions Sensitivity of the Data Potential Injury to Consumers Appropriateness of Consent General Information on Mitigating Controls Introduction Origins of the MCI Subject of Assessment: Data Requestor versus Data Recipient Applicability of the MCI Structure of the MCI Scoring Which Practices to Rate Third-Party versus Self-Assessment Scoring the MCI Interpreting to the MCI Questions General Justifications for Time Intervals Practical Requirements Remediation Controlling Access, Disclosure, Retention, and Disposition of Personal Data Safeguarding Personal Data Ensuring Accountability and Transparency in the Management of Personal Data Assessing Motives and Capacity Dimensions Motives to Re-identify the Data Capacity to Re-identify the Data Invasion of Privacy Sensitivity of the Data Potential Injury to Patients Appropriateness of Consent Index --此文字指其他 kindle_edition 版本。
基本信息
- ASIN : B00CLZT7L0
- 出版社 : Auerbach Publications; 第 1st 版 (2013年5月6日)
- 出版日期 : 2013年5月6日
- 语言 : 英语
- 文件大小 : 27532 KB
- 标准语音朗读 : 未启用
- X-Ray : 未启用
- 生词提示功能 : 未启用
- 纸书页数 : 413页
- 亚马逊热销商品排名: 商品里排第299,391名Kindle商店 (查看商品销售排行榜Kindle商店)
- 商品里排第160名Information Science（信息科技）
- 商品里排第189名Networking & Security（网络与安全）
- 商品里排第556名Computer Business & Management（计算机商业与管理）
- 用户评分:
无顾客评论
| 5 星 (0%) |
|
0% |
| 4 星 (0%) |
|
0% |
| 3 星 (0%) |
|
0% |
| 2 星 (0%) |
|
0% |
| 1 星 (0%) |
|
0% |
评分是如何计算的？
在计算总星级评分以及按星级确定的百分比时，我们不使用简单的平均值。相反，我们的系统会考虑评论的最新程度以及评论者是否在亚马逊上购买了该商品。系统还会分析评论，验证评论的可信度。
此商品在美国亚马逊上最有用的商品评论
美国亚马逊：
5.0 颗星，最多 5 颗星
2 条评论
Bronk
5.0 颗星，最多 5 颗星
great overview on the topic
2018年3月29日 -
已在美国亚马逊上发表已确认购买
great overview on the topic. If you want to de-identify data (without ruining its usefulness), this is a great start and resource to help educate others on your team .
%2520%25E4%25BD%259C%25E8%2580%2585%2520Khaled%2520El%2520Emam%2520%25EF%25BC%258C%25E7%258B%25AC%25E4%25B9%2590%25E4%25B9%2590%25E4%25B8%258D%25E5%25A6%2582%25E4%25BC%2597%25E4%25B9%2590%25E4%25B9%2590%25EF%25BC%2581%25E5%2588%2586%25E4%25BA%25AB%25E7%25BB%2599%25E5%25A4%25A7%25E5%25AE%25B6%25E3%2580%2582%26desc%3D%25E5%25BE%2597%25E6%259D%25A5%25E5%2585%25A8%25E4%25B8%258D%25E8%25B4%25B9%25E5%25B7%25A5%25E5%25A4%25AB~%25E6%2588%2591%25E5%259C%25A8%25E4%25BA%259A%25E9%25A9%25AC%25E9%2580%258AZ.cn%2520%25E6%2589%25BE%25E5%2588%25B0%25E4%25BA%2586%2520Guide%2520to%2520the%2520De-Identification%2520of%2520Personal%2520Health%2520Information%2520(English%2520Edition)%2520%25E4%25BD%259C%25E8%2580%2585%2520Khaled%2520El%2520Emam%2520%25EF%25BC%258C%25E7%258B%25AC%25E4%25B9%2590%25E4%25B9%2590%25E4%25B8%258D%25E5%25A6%2582%25E4%25BC%2597%25E4%25B9%2590%25E4%25B9%2590%25EF%25BC%2581%25E5%2588%2586%25E4%25BA%25AB%25E7%25BB%2599%25E5%25A4%25A7%25E5%25AE%25B6%25E3%2580%2582%26url%3Dhttps%253A%252F%252Fwww.amazon.cn%252Fdp%252FB00CLZT7L0%252Fref%253Dcm_sw_r_qq_dp_XYRFS64YV3Z0Z01AHWBW%26pics%3Dhttps%253A%252F%252Fimages-cn.ssl-images-amazon.com%252Fimages%252FI%252F41mhTryWDqL.jpg%26site%3DAmazon&token=3CF46E623CFF7D3D9FE930E2DDA86C8D020005B7){kind=link}
%2520%25E4%25BD%259C%25E8%2580%2585%2520Khaled%2520El%2520Emam%2520%25E3%2580%2582%26pic%3Dhttps%253A%252F%252Fimages-cn.ssl-images-amazon.com%252Fimages%252FI%252F41mhTryWDqL.jpg%26url%3Dhttps%253A%252F%252Fwww.amazon.cn%252Fdp%252FB00CLZT7L0%252Fref%253Dcm_sw_r_si_dp_XYRFS64YV3Z0Z01AHWBW%26appkey%3D620381579%26ralateUid%3D1732523361&token=35A62F94781608114E73C0C33526F0DAB727BB68){kind=link}
%2520%25E4%25BD%259C%25E8%2580%2585%2520Khaled%2520El%2520Emam%26title%3D%25E6%2588%2591%25E5%258F%25AA%25E8%25B4%25AD%25E4%25B9%25B0Guide%2520to%2520the%2520De-Identification%2520of%2520Personal%2520Health%2520Information%2520(English%2520Edition)%2520%25E4%25BD%259C%25E8%2580%2585%2520Khaled%2520El%2520Emam%26pics%3Dhttps%253A%252F%252Fimages-cn.ssl-images-amazon.com%252Fimages%252FI%252F41mhTryWDqL.jpg%26url%3Dhttps%253A%252F%252Fwww.amazon.cn%252Fdp%252FB00CLZT7L0%252Fref%253Dcm_sw_r_qz_dp_XYRFS64YV3Z0Z01AHWBW&token=47A32C8C0E8C617E6B307B78C7A154CB3A1D4A31){kind=link}