Josep Domingo-Ferrer – författare

A reference to answer all your statistical confidentiality questions.

This handbook provides technical guidance on statistical disclosure control and on how to approach the problem of balancing the need to provide users with statistical outputs and the need to protect the confidentiality of respondents. Statistical disclosure control is combined with other tools such as administrative, legal and IT in order to define a proper data dissemination strategy based on a risk management approach.

The key concepts of statistical disclosure control are presented, along with the methodology and software that can be used to apply various methods of statistical disclosure control. Numerous examples and guidelines are also featured to illustrate the topics covered.

Statistical Disclosure Control:

Official statisticians, academics and market researchers who need to be informed and make decisions on disclosure limitation will benefit from this book. 

A reference to answer all your statistical confidentiality questions.

This handbook provides technical guidance on statistical disclosure control and on how to approach the problem of balancing the need to provide users with statistical outputs and the need to protect the confidentiality of respondents. Statistical disclosure control is combined with other tools such as administrative, legal and IT in order to define a proper data dissemination strategy based on a risk management approach.

The key concepts of statistical disclosure control are presented, along with the methodology and software that can be used to apply various methods of statistical disclosure control. Numerous examples and guidelines are also featured to illustrate the topics covered.

Statistical Disclosure Control:

Official statisticians, academics and market researchers who need to be informed and make decisions on disclosure limitation will benefit from this book. 

This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2020, held in Tarragona, Spain, in September 2020 under the sponsorship of the UNESCO Chair in Data Privacy.The 25 revised full papers presented were carefully reviewed and selected from 49 submissions. The papers are organized into the following topics: privacy models; microdata protection; protection of statistical tables; protection of interactive and mobility databases; record linkage and alternative methods; synthetic data; data quality; and case studies.The Chapter “Explaining recurrent machine learning models: integral privacy revisited” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

​This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2024, held in Antibes Juan-les-Pins, France, during September 25-27, 2024.

The 28 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections as follows: Privacy models and concepts; Microdata protection; Statistical table protection; Synthetic data generation methods; Synthetic data generation software; Disclosure risk assessment; Spatial and georeferenced data; Machine learning and privacy; and Case studies. 

This book constitutes the refereed proceedings of the International Conference on Privacy in Statistical Databases, PSD 2016, held in Dubrovnik, Croatia in September 2016 under the sponsorship of the UNESCO chair in Data Privacy. The 19 revised full papers presented were carefully reviewed and selected from 35 submissions. The scope of the conference is on following topics: tabular data protection; microdata and big data masking; protection using privacy models; synthetic data; remote and cloud access; disclosure risk assessment; co-utile anonymization.

The 23 revised full papers presented were carefully reviewed and selected from 42 submissions. The papers are organized into the following topics: tabular data protection; synthetic data; microdata and big data masking; record linkage; and spatial and mobility data.

Chapter "SwapMob: Swapping Trajectories for Mobility Anonymization" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.