Handbook of Sharing Confidential Data: Differential Privacy, Secure Multiparty Computation, and Synthetic Data
暫譯: 機密數據共享手冊：差分隱私、安全多方計算與合成數據

《機密數據共享手冊》幫助數據管理者了解如何利用數據機密性文獻中的工具，特別是合成數據、正式隱私和安全計算，來管理披露風險和數據實用性之間的權衡。

Jörg Drechsler is Head of the Department for Statistical Methods at the Institute for Employment Research in Nuremberg, Germany, and Professor of Statistical Science at the Institute for Statistics at the Ludwig-Maximilians-University in Munich. He is also Associate Research Professor in the Joint Program in Survey Methodology at the University of Maryland. His main research interests are data confidentiality and nonresponse in surveys. He is a fellow of the International Statistical Institute. He received his PhD in Social Science from the University of Bamberg and his Habilitation in Statistics from the Ludwig-Maximilians-Universität in Munich.

Daniel Kifer is Professor of Computer Science at Penn State University. He has published extensively on technical approaches for privacy and confidentiality, with work spanning attack algorithms, novel methods for disclosure avoidance, statistical analysis of perturbed data, and automated tools for catching implementation errors. In 2016-2017, Kifer spent his sabbatical at the U.S. Census Bureau and helped design the disclosure avoidance system used for the 2020 Decennial Census. Kifer obtained his bachelor's degrees in mathematics and computer science at New York
University and his PhD at Cornell.

Jerome Reiter is Professor of Statistical Science at Duke University. His primary research areas include methods for protecting data confidentiality, for handling missing values, and for integrating data across multiple sources. He has worked extensively on theory, methods, and applications for synthetic data. He is Fellow of the Institute of Mathematical Statistics and the American Statistical Association. He received a PhD in statistics from Harvard University and his undergraduate degree from Duke University.

Aleksandra Slavkovic is Professor of Statistics & Public Health Sciences, Dorothy Foehr Huck and J.Lloyd Huck Chair in Data Privacy and Confidentiality, and Associate Dean for Research, Eberly College of Science at Penn State. Her research focuses on methodological developments in the area of data privacy and confidentiality in the context of small- and large-scale surveys, health, genomic, and network data, including work on differential privacy and broad data access that offers guarantees of accurate statistical inference needed to support reliable science and policy. She is Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the International Statistical Institute. She received her PhD (2004) and MS (2001) in statistics and Master of Human-Computer Interaction (1999) from Carnegie Mellon University. She received her BA in psychologyfrom Duquesne University (1996).

約爾格·德雷克斯勒是德國紐倫堡就業研究所統計方法部門的負責人，並且是慕尼黑路德維希-馬克西米利安大學統計學研究所的統計科學教授。他也是馬里蘭大學聯合調查方法計畫的副研究教授。他的主要研究興趣包括數據保密性和調查中的非回應問題。他是國際統計學會的會員。他在班貝格大學獲得社會科學博士學位，並在慕尼黑的路德維希-馬克西米利安大學獲得統計學的Habilitation學位。

丹尼爾·基弗是賓州州立大學的計算機科學教授。他在隱私和保密的技術方法方面發表了大量的研究，涵蓋了攻擊算法、避免披露的新方法、擾動數據的統計分析以及捕捉實施錯誤的自動化工具。在2016年至2017年期間，基弗在美國人口普查局度過了他的學術休假，並幫助設計了用於2020年十年一次人口普查的披露避免系統。基弗在紐約大學獲得數學和計算機科學的學士學位，並在康奈爾大學獲得博士學位。

傑羅姆·雷特是杜克大學的統計科學教授。他的主要研究領域包括保護數據保密性的方法、處理缺失值的方法以及跨多個來源整合數據的方法。他在合成數據的理論、方法和應用方面有廣泛的研究。他是數學統計學會和美國統計學會的會員。他在哈佛大學獲得統計學博士學位，並在杜克大學獲得本科学位。

亞歷山德拉·斯拉夫科維奇是賓州州立大學統計學與公共衛生科學的教授，擔任多蘿西·福爾·哈克和J. Lloyd Huck數據隱私與保密的講座教授，以及科學學院的研究副院長。她的研究專注於小型和大型調查、健康、基因組和網絡數據中數據隱私和保密的研究方法發展，包括差異隱私和廣泛數據訪問的工作，這些工作提供了支持可靠科學和政策所需的準確統計推斷的保證。她是美國統計學會、數學統計學會和國際統計學會的會員。她在卡內基梅隆大學獲得統計學博士（2004年）和碩士（2001年）學位，以及人機互動碩士（1999年）學位。她在杜奎斯尼大學獲得心理學學士學位（1996年）。