This book provides modern technical answers to the legal requirements of pseudonymisation as recommended by privacy legislation. It covers topics such as modern regulatory frameworks for sharing and linking sensitive information, concepts and algorithms for privacy-preserving record linkage and their computational aspects, practical considerations such as dealing with dirty and missing data, as well as privacy, risk, and performance assessment measures. Existing techniques for privacy-preserving record linkage are evaluated empirically and real-world application examples that scale to population sizes are described. The book also includes pointers to freely available software tools, benchmark data sets, and tools to generate synthetic data that can be used to test and evaluate linkage techniques. This book consists of fourteen chapters grouped into four parts, and two appendices. The first part introduces the reader to the topic of linking sensitive data, the second part covers methods and techniques to link such data, the third part discusses aspects of practical importance, and the fourth part provides an outlook of future challenges and open research problems relevant to linking sensitive databases. The appendices provide pointers and describe freely available, open-source software systems that allow the linkage of sensitive data, and provide further details about the evaluations presented. A companion Web site at https://dmm.anu.edu.au/lsdbook2020 provides additional material and Python programs used in the book. This book is mainly written for applied scientists, researchers, and advanced practitioners in governments, industry, and universities who are concerned with developing, implementing, and deploying systems and tools to share sensitive information in administrative, commercial, or medical databases. The Book describes how linkage methods work and how to evaluate their performance. It covers all the major concepts and methods and also discusses practical matters such as computational efficiency, which are critical if the methods are to be used in practice - and it does all this in a highly accessible way!David J. Hand, Imperial College, London

This book provides modern technical answers to the legal requirements of pseudonymisation as recommended by privacy legislation. It covers topics such as modern regulatory frameworks for sharing and linking sensitive information, concepts and algorithms for privacy-preserving record linkage and their computational aspects, practical considerations such as dealing with dirty and missing data, as well as privacy, risk, and performance assessment measures. Existing techniques for privacy-preserving record linkage are evaluated empirically and real-world application examples that scale to population sizes are described. The book also includes pointers to freely available software tools, benchmark data sets, and tools to generate synthetic data that can be used to test and evaluate linkage techniques. This book consists of fourteen chapters grouped into four parts, and two appendices. The first part introduces the reader to the topic of linking sensitive data, the second part covers methods and techniques to link such data, the third part discusses aspects of practical importance, and the fourth part provides an outlook of future challenges and open research problems relevant to linking sensitive databases. The appendices provide pointers and describe freely available, open-source software systems that allow the linkage of sensitive data, and provide further details about the evaluations presented. A companion Web site at https://dmm.anu.edu.au/lsdbook2020 provides additional material and Python programs used in the book. This book is mainly written for applied scientists, researchers, and advanced practitioners in governments, industry, and universities who are concerned with developing, implementing, and deploying systems and tools to share sensitive information in administrative, commercial, or medical databases. The Book describes how linkage methods work and how to evaluate their performance. It covers all the major concepts and methods and also discusses practical matters such as computational efficiency, which are critical if the methods are to be used in practice - and it does all this in a highly accessible way!David J. Hand, Imperial College, London

Zu den wichtigsten praktischen Problemen, vor denen standardisierte Daten erhebungen in den Sozialwissenschaften stehen, gehort das Nonresponse-Pro blemI. Obwohl es wenige empirische Belege dafur gibt, wird allgemein an genommen, daB sich ein immer groBerer Teil der fur eine Befragung ausge wahlten Personen der Untersuchung entzieht. Empirische Untersuchungen der Entwicklung des Nonresponse sind hingegen selten. Da fur die Bundesrepu blik bislang nur wenige Vergleichsdaten existieren, wurde eine Nonresponse Zeitreihe erstellt, deren Analyse einen Schwerpunkt dieser Arbeit bildet. Von groBter Bedeutung fur den Umgang mit Nonresponse ist eine Theo rie des Teilnahmeverhaltens. Eine solche Theorie existiert bisher nur in An satzen. Die umfangreiche Nonresponseliteratur enthalt zwar ei ne Fulle einzel ner empirischer Resultate und Hypothesen, die aber weitgehend isoliert nebeneinander stehen. In der neueren Literatur besteht weitgehend Einigkeit daruber, daB eine Theorie des Teilnahmeverhaltens eine Variante einer Aus tauschtheorie bzw. einer Rational-Choice-Theorie sein wird. In dieser Arbeit wird versucht, alle Theoriefragmente sowie alle empirisch bewahrten Einzel hypothesen als Spezialfall einer solchen Rational-Choice-Theorie abzuleiten. Neben den Ausfallen durch Verweigerungen kommt den Ausfallen durch Nichterreichbarkeit groBe Bedeutung zu. In einem eigenen Kapitel wird auf ModelIe zur Erklarung der Erreichbarkeit eingegangen. Hierbei wird den Einzelheiten der Feldarbeit in der BRD in Hinsicht auf Unterschiede zwi schen den Erhebungsinstituten besondere Aufmerksamkeit gewidmet."

Der "Schnell" behandelt Techniken zur graphischen Darstellung von Daten oder statistischer Grosse im Rahmen von Datenanalysen. Diese "Datenanalysegraphik" ist ein nutzliches Instrument fur Datenanalytiker, hier wiederum bevorzugt solche in den Sozialwissenschaften."