Building an Anonymization Pipeline: Creating Safe Data
暫譯: 建立匿名化管道：創建安全數據

如何在保護個人隱私的同時，使用數據提供有用且有意義的分析？這本實用的書籍將幫助數據架構師和工程師學習如何以可持續的方式建立和整合安全、可重複的匿名化過程到他們的數據流和分析中。

來自 Privacy Analytics 的 Luk Arbuckle 和 Khaled El Emam 探討了針對設備和物聯網（IoT）數據的端到端匿名化解決方案，這些解決方案基於滿足實際商業需求的收集模型和使用案例。這些例子來自一些最具挑戰性的數據環境，例如醫療保健，並採用經得起時間考驗的方法。

- 創建足夠多樣化的匿名化解決方案，以涵蓋各種使用案例
- 將您的解決方案與您使用的數據、您分享的對象以及您的分析目標相匹配
- 圍繞各種數據收集模型構建匿名化管道，以滿足不同的商業需求
- 生成原始數據的匿名版本或使用分析平台生成匿名輸出
- 檢視使用匿名數據的倫理問題

Luk Arbuckle is Chief Methodologist at Privacy Analytics, providing strategic leadership in how to responsibly share and use data. Luk was previously Director of Technology Analysis at the Office of the Privacy Commissioner of Canada leading a highly skilled team that conducted privacy research and assisted in investigations when there was a technology component involved. Before joining the Office of the Privacy Commissioner of Canada, Luk worked on developing de-identification methods and re-identification risk measurement tools, participated in the development and evaluation of secure computation protocols, and led a top-notch research and consulting team that developed and delivered data anonymization solutions. Luk originally plied his trade in the area of image processing and analysis, and then in the area of applied statistics (use R!).

Dr. Khaled El Emam is a senior scientist at the Children's Hospital of Eastern Ontario (CHEO) Research Institute and Director of the multi-disciplinary Electronic Health Information Laboratory, conducting applied academic research on synthetic data generation methods and tools, and re-identification risk measurement. He is also a Professor in the Faculty of Medicine (Pediatrics) at the University of Ottawa, Canada.

Khaled is the co-founder and CEO of Replica Analytics, a company focused on the development of synthetic data to drive the application of AIML in the healthcare industry. He is also the founder, and was until the end of 2019 the General Manager and President of Privacy Analytics, which was acquired by IMS Health (now IQVIA)in 2016. He currently invests, advises, and sits on the boards of technology companies developing data protection technologies, and building analytics tools to support healthcare delivery and drug discovery.

He has been performing data analysis since the early 90`s, building statistical and machine learning models for prediction and evaluation. Since 2004 he has been developing technologies to facilitate the sharing of data for secondary analysis, from basic research on algorithms to applied solutions development that have been deployed globally. These technologies addressed problems in anonymization & pseudonymization, synthetic data, secure computation, and data watermarking.

He has (co-)written and (co-)edited multiple books on various privacy and software engineering topics. 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.

Previously, Khaled was a Senior Research Officer at the National Research Council of Canada. He also served as the head of the Quantitative Methods Group at the Fraunhofer Institute in Kaiserslautern, Germany. He held the Canada Research Chairin Electronic Health Information at the University of Ottawa from 2005 to 2015, and has a PhD from the Department of Electrical and Electronics Engineering, King's College, at the University of London, England.

Luk Arbuckle 是 Privacy Analytics 的首席方法學家，負責提供有關如何負責任地分享和使用數據的戰略領導。Luk 之前是加拿大隱私專員辦公室的技術分析主任，領導一支高技能團隊，進行隱私研究並在涉及技術組件的調查中提供協助。在加入加拿大隱私專員辦公室之前，Luk 專注於開發去識別化方法和再識別風險測量工具，參與安全計算協議的開發和評估，並領導一支一流的研究和諮詢團隊，開發並提供數據匿名化解決方案。Luk 最初在影像處理和分析領域工作，然後轉向應用統計學（使用 R！）。

Khaled El Emam 博士是東安大略兒童醫院（CHEO）研究所的高級科學家，也是多學科電子健康信息實驗室的主任，進行有關合成數據生成方法和工具以及再識別風險測量的應用學術研究。他同時也是加拿大渥太華大學醫學院（小兒科）的教授。

Khaled 是 Replica Analytics 的共同創辦人和首席執行官，該公司專注於開發合成數據，以推動 AIML 在醫療行業的應用。他也是 Privacy Analytics 的創始人，並在2019年底之前擔任總經理和總裁，該公司於2016年被 IMS Health（現為 IQVIA）收購。他目前投資、提供建議，並在開發數據保護技術和構建支持醫療服務及藥物發現的分析工具的科技公司董事會任職。

自90年代初以來，他一直在進行數據分析，建立統計和機器學習模型以進行預測和評估。自2004年以來，他一直在開發促進數據共享以進行二次分析的技術，從算法的基本研究到已在全球部署的應用解決方案。這些技術解決了匿名化和假名化、合成數據、安全計算和數據水印等問題。

他（共同）撰寫和（共同）編輯了多本有關各種隱私和軟體工程主題的書籍。在2003年和2004年，他因其在測量和質量評估及改進方面的研究，被《系統與軟體期刊》評選為全球頂尖的系統和軟體工程學者。

之前，Khaled 是加拿大國家研究委員會的高級研究官。他還曾擔任德國凱瑟斯勞滕弗勞恩霍夫研究所的定量方法組組長。他於2005年至2015年在渥太華大學擔任電子健康信息的加拿大研究主席，並擁有英國倫敦大學國王學院電氣與電子工程系的博士學位。

From the Preface

When conceptualizing this book, we divided the audience in two groups: those who need strategic support (our primary audience) and those who need to understand strategic decisions (our secondary audience). Whether in government or industry, it is a functional need to deliver on the promise of data. We assume that our audience is ready to do great things, beyond compliance with data privacy and data protection laws. And we assume that they are looking for data access models, to enable the safe and responsible use of data.

Primary audience (concerned with crafting a vision and ensuring the successful execution of that vision):

• Executive teams concerned with how to make the most of data, e.g., to improve efficiencies, derive new insights, and bring new products to market, all in an effort to make their services broader and better while enhancing the privacy of data subjects. They are more likely to skim this book to nail down their vision and how anonymization fits within it.

• Data architects and data engineers who need to match their problems to privacy solutions, thereby enabling secure and privacy-preserving analytics. They are more likely to home in on specific details and considerations to help support strategic decisions and figure out the specifics they need for their use cases.

From the Preface

When conceptualizing this book, we divided the audience in two groups: those who need strategic support (our primary audience) and those who need to understand strategic decisions (our secondary audience). Whether in government or industry, it is a functional need to deliver on the promise of data. We assume that our audience is ready to do great things, beyond compliance with data privacy and data protection laws. And we assume that they are looking for data access models, to enable the safe and responsible use of data.

Primary audience (concerned with crafting a vision and ensuring the successful execution of that vision):

• Executive teams concerned with how to make the most of data, e.g., to improve efficiencies, derive new insights, and bring new products to market, all in an effort to make their services broader and better while enhancing the privacy of data subjects. They are more likely to skim this book to nail down their vision and how anonymization fits within it.

• Data architects and data engineers who need to match their problems to privacy solutions, thereby enabling secure and privacy-preserving analytics. They are more likely to home in on specific details and considerations to help support strategic decisions and figure out the specifics they need for their use cases.