Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data

Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data--fake data generated from real data--so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue.

Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution.

This book describes:

• Steps for generating synthetic data using multivariate normal distributions
• Methods for distribution fitting covering different goodness-of-fit metrics
• How to replicate the simple structure of original data
• An approach for modeling data structure to consider complex relationships
• Multiple approaches and metrics you can use to assess data utility
• How analysis performed on real data can be replicated with synthetic data
• Privacy implications of synthetic data and methods to assess identity disclosure

建立和測試機器學習模型需要大量且多樣化的數據。但是，在不涉及隱私問題的情況下，你在哪裡可以找到可用的數據集呢？這本實用書介紹了生成合成數據的技術，即從真實數據生成的假數據，以便進行二次分析，進行研究，了解客戶行為，開發新產品或產生新收入。

數據科學家將學習到合成數據生成如何提供一種廣泛可用於二次目的的數據，同時解決許多隱私問題。分析師將學習從真實數據集生成合成數據的原則和步驟。企業領導者將看到合成數據如何幫助加快產品或解決方案的時間。

本書描述了以下內容：

- 使用多變量正態分佈生成合成數據的步驟
- 涵蓋不同適配度指標的分佈擬合方法
- 如何複製原始數據的簡單結構
- 考慮複雜關係的數據結構建模方法
- 您可以使用的多種方法和指標來評估數據效用
- 如何使用合成數據複製對真實數據進行的分析
- 合成數據的隱私影響以及評估身份泄露的方法

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 academic research on synthetic data generation methods, and re- identification risk measurement, and he is also a Professor in the Faculty of Medicine (Pediatrics) at the University of Ottawa.

He is the founder, CEO, and President of Privacy Analytics. Khaled has been performing data analysis since the early 90s, 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 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 Chair in 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.

Lucy Mosquera has a bachelor's degree in Biology and Mathematics from Queen's University and is a current graduate student in the department of statistics at the University of British Columbia. During her time at Queen's, Lucy provided data management support on a dozen clinical trials and observational studies run through Kingston General Hospital's Clinical Evaluation Research Unit. Lucy has also worked on clinical trial data sharing methods based on homomorphic encryption and secret sharing protocols. At Replica Analytics, Lucy is responsible for developing statistical and machine learning models for data generation, and integrating subject area expertise in clinical trial data into synthetic data generation methods, as well as the statistical assessments of our synthetic data generation.

Dr. Richard Hoptroff is a long term technology inventor, investor and entrepreneur. Awarded a PhD in Physics by King's College London for his work in optical computing and artificial intelligence, in 1992, together with Ravensbeck, he founded Right Information Systems, a neural network forecasting software company which was in 1997 sold to Cognos Inc (part of IBM). He then worked as a postdoc at the Research Laboratory for Archaeology and the History of Art at Oxford University and in 2001, created Flexipanel Ltd, a company supplying Bluetooth modules to the electronics industry.

In 2010, he founded the Hoptroff London, with the aim to develop smart, hyper-accurate watch movements and create a new watch brand. In 2013 he established a new commercial category when he brought to market the first commercial atomic timepiece and atomic wristwatch.

Hoptroff has now leveraged his expertise in timing technology and software to develop a hyper- accurate synchronised timestamping solution for the financial services sector, based on a unique combination of grandmaster atomic clock engineering and proprietary software.

Dr. Khaled El Emam是加拿大東部兒童醫院（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，從事合成數據生成方法和重新識別風險測量的學術研究，同時也是渥太華大學醫學院（兒科）的教授。

他是Privacy Analytics的創始人、首席執行官和總裁。Khaled從90年代初開始進行數據分析，建立統計和機器學習模型進行預測和評估。自2004年以來，他一直在開發技術，以促進數據在二次分析中的共享，從基礎算法的基礎研究到應用解決方案的全球部署。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（合著）撰寫了多本關於隱私和軟件工程等不同主題的書籍。根據《系統和軟件》雜誌的研究，他在2003年和2004年被評為全球頂尖的系統和軟件工程學者，該研究基於他在測量和質量評估和改進方面的研究。此前，Khaled曾在加拿大國家研究委員會擔任高級研究員。他還曾擔任德國凱澤斯勞滕的Fraunhofer研究所的量化方法組組長。他在渥太華大學擔任電子健康信息的加拿大研究主席，從2005年到2015年，並且擁有倫敦大學國王學院電氣和電子工程系的博士學位。

Lucy Mosquera擁有皇后大學的生物學和數學學士學位，目前是英屬哥倫比亞大學統計學系的研究生。在皇后大學期間，Lucy在金斯頓綜合醫院臨床評估研究單位提供數據管理支持，參與了十幾個臨床試驗和觀察研究。Lucy還從事基於同態加密和秘密共享協議的臨床試驗數據共享方法的研究。在Replica Analytics，Lucy負責開發統計和機器學習模型進行數據生成，並將臨床試驗數據領域專業知識整合到合成數據生成方法中，以及對我們的合成數據生成進行統計評估。

Dr. Richard Hoptroff是長期從事技術發明、投資和創業的人。他在倫敦大學國王學院獲得物理學博士學位，研究光學計算和人工智能。1992年，他與Ravensbeck共同創立了Right Information Systems，一家神經網絡預測軟件公司，該公司於1997年被Cognos Inc（IBM的一部分）收購。然後他在牛津大學考古學和藝術歷史研究實驗室擔任博士後研究員，並於2001年創立了Flexipanel Ltd，一家為電子行業提供藍牙模塊的公司。

2010年，他創立了Hoptroff London，旨在開發智能、超精確的手錶機芯並創建新的手錶品牌。2013年，他推出了第一款商業原子計時器和原子手錶，創立了一個新的商業類別。

Hoptroff現在利用他在計時技術和軟件方面的專業知識，開發了一個超精確的金融服務部門的同步時間戳解決方案，該解決方案基於獨特的大師原子時鐘工程和專有軟件。