National statistics agencies are mandated to collect microdata [1] from surveys and censuses to inform and measure policy effectiveness. In almost all countries, statistics acts and privacy laws govern these activities. These laws require that agencies protect the identity of respondents, but may also require that agencies disseminate the results and, in appropriate cases, the microdata. Data producers who are not part of national statistics agencies are also often subject to restrictions, through privacy laws or strict codes of conduct and ethics that require a similar commitment to privacy protection. This has to be balanced against the increasing requirement from funders that data produced using donor funds be made publically available.

This tension between complying with confidentiality requirements while at the same time requiring that microdata be released means that a demand exists for practical solutions for applying Statistical Disclosure Control (SDC), also known as microdata anonymization. The provision of adequate solutions and technical support has the potential to “unlock” a large number of datasets.

The International Household Survey Network (IHSN) and the World Bank have contributed to successful programs that have generated tools, resources and guidelines for the curation, preservation and dissemination of microdata and resulted in the documentation of thousands of surveys by countries and agencies across the world. While these programs have ensured substantial improvements in the preservation of data and dissemination of good quality metadata, many agencies are still reluctant to allow access to the microdata. The reasons are technical, legal, ethical and political, and sometimes involve a fear of being criticized for not providing perfect data. When combined with the tools and guidelines already developed by the IHSN/World Bank for the curation, preservation and dissemination of microdata, tools and guidelines for the anonymization of microdata should further reduce or remove some of these obstacles.

Working with the IHSN, PARIS21 (OECD), Statistics Austria and the Vienna University of Technology, the World Bank has contributed to the development of an open source software package for SDC, called sdcMicro. The package was developed for use with the open source R statistical software, available from the Comprehensive R Archive Network (CRAN) at The package includes numerous methods for the assessment and reduction of disclosure risk in microdata.

Ensuring that a free open source solution is available to agencies was an important step forward, but not a sufficient one. There is still limited consolidated and reported knowledge on the impact of disclosure risk reduction methods on data utility. This limited access to knowledge combined with a lack of experience in using the tools and methods makes it difficult for many agencies to implement optimal solutions, i.e., solutions that meet their obligations towards both privacy protection and the release of data useful for policy monitoring and evaluation. This practice guide attempts to fill this critical gap by:

  1. consolidating knowledge gained at the World Bank through experiments conducted during a large-scale evaluation of anonymization techniques
  2. translating the experience and key results into practical guidelines

It should be stressed that SDC is only one part of the data release process, and its application must be considered within the complete data release framework. The level and methods of SDC depend on the laws of the country, the sensitivity of the data and the access policy (i.e., who will gain access) considered for release. Agencies that are currently releasing data are already using many of the methods described in this guide and applying appropriate access polices to their data before release. The primary objective of this guide is to provide a primer to those new to the process who are looking for guidance on both theory and practical implementation. This guide is not intended to prescribe or advocate for changes in methods that specific data producers are already using and which they have designed to fit and comply with their existing data release policies.

The guide seeks to provide practical steps to those agencies that want to unlock access to their data in a safe way and ensure that the data remain fit for purpose.

Building a knowledge base

The release of data is important, as it allows researchers and policymakers to replicate officially published results, generate new insights into issues, avoid duplication of surveys and provide greater returns to the investment in the survey process.

Both the production of reports, with aggregate tables of indicators and statistics, and the release of microdata result in privacy challenges to the producer. In the past, for many agencies, the only requirement was to release a report and some key indicators. The recent movement around Open Data, Open Government and transparency means that agencies are under greater pressure to release their microdata to allow broader use of data collected through public and donor funds. This guide focuses on the methods and processes for the release of microdata.

Releasing data in a safe way is required to protect the integrity of the statistical system, by ensuring agencies honor their commitment to respondents to protect their identity. Agencies do not widely share, in substantial detail, their knowledge and experience using SDC and the processes for creating safe data with other agencies. This makes it difficult for agencies new to the process to implement solutions. To fill this experience and knowledge gap, we evaluated the use of a broad suite of SDC methods on a range of survey microdata covering important development topics related to health, labor, education, poverty and inequality. The data we used were all previously treated to make them safe for release. Given that their producers had already treated these data, it was not possible, nor was it our goal, to pass any judgment on the safety of these data, many of which are in the public domain The focus was rather on measuring the effects that various methods would have on the risk-utility trade-off for microdata produced to measure common development indicators. We used the experience from this large-scale experimentation to inform our discussion of the processes and methods in this guide.


At no point was any attempt made to re-identify, through matching or any other method, any respondents in the surveys we used in building our knowledge base. All risk assessments were based on frequencies and probabilities.

Using this guide

The methods discussed in this guide originate from a large body of literature on SDC. The processes underlying many of the methods are the subject of extensive academic research and many, if not all, of them are used extensively by agencies experienced in preparing microdata for release.

Where possible, for each method and topic, we provide elaborate examples, references to the original or seminal work describing the methods and algorithms in detail and recommended readings. This, when combined with the discussion of the method and practical considerations in this guide, should allow the reader to understand the methods and their strengths and weaknesses. It should also provide enough detail for readers to use an existing software solution to implement the methods or program the methods in statistical software of their choice.

For the examples in this guide, we use the open source and free package for SDC called sdcMicro as well as the statistical software R. sdcMicro is an add-on package to the statistical software R. The package was developed and is maintained by Matthias Templ, Alexander Kowarik and Bernhard Meindl. [2] The statistical software R and the sdcMicro package, as well as any other packages needed for the SDC process, are freely available from the Comprehensive R Archive Network (CRAN) mirrors ( The software is available for Linux, Windows and Macintosh operating systems. We chose to use R and sdcMicro because it is freely available, accommodates all main data formats and is easy to adapt by the user. The World Bank, through the IHSN, has also provided funding towards the development of the sdcMicro package to ensure it meets the requirements of the agencies we support.

This guide does not provide a review of all other available packages for implementing the SDC process. Our concern is more with providing practical insight into the application of the methods. We would, however, like to highlight one particular other software package that is commonly used by agencies: μ-ARGUS [3]. μ-ARGUS is developed by Statistics Netherlands. sdcMicro and μ-ARGUS are both widely used in statistics offices in the European Union and implement many of the same methods.

The user needs some knowledge of R to use sdcMicro. It is beyond the scope of this guide to teach the use of R, but we do provide throughout the guide code examples on how to implement the necessary routines in R. [4] We also present a number of case studies that include the code for the anonymization of a number of demo datasets using R. Through these case studies, we demonstrate a number of approaches to the anonymization process in R. [5]

Outline of this guide

This guide is divided into the following main sections:

  1. the Section Statistical Disclosure Control (SDC): An Introduction is a primer on SDC.
  2. the Section Release Types gives an introduction to different release types for microdata.
  3. the Sections Anonymization Methods , Measuring Risk and Measuring Utility and Information Loss cover SDC methods, risk and utility measurement. The goal here is to provide knowledge that allows the reader to independently apply and execute the SDC process. This section is enriched with real examples as well as code snippets from the sdcMicro package. The interested reader can also find more information in the references and recommended readings at the end of each section.
  4. the Section SDC with sdcMicro in R: Setting Up Your Data and more gives an overview of issues encountered when carrying out anonymization with the sdcMicro package in R, which exceed basic R knowledge. This section also includes tips and solutions to some of the common issues and problems that might be encountered when applying SDC methods in R with sdcMicro.
  5. the Section The SDC Process provides a step-by-step guide to disclosure control, which draws upon the knowledge presented in the previous sections.
  6. the Section Case Studies (Illustrating the SDC Process) presents a number of detailed case studies that demonstrate the use of the methods, their implementation in sdcMicro and the process that should be followed to reach the optimal risk-utility solution.
[1]Microdata are unit-level data obtained from sample surveys, censuses and administrative systems. They provide information about characteristics of individual people or entities such as households, business enterprises, facilities, farms or even geographical areas such as villages or towns. They allow in-depth understanding of socio-economic issues by studying relationships and interactions among phenomena. Microdata are thus key to designing projects and formulating policies, targeting interventions and monitoring and measuring the impact and results of projects, interventions and policies.
[2]See and the GitHub of the developers. The GitHub repository can also be used to submit bugs found in the package.
[3]μ-ARGUS is available at: The software was recently ported to open source.
[4]There are many free resources for learning R available on the web. One place to start would be the CRAN R Project page:
[5]The developers of sdcMicro have also developed a graphical user interface (GUI) for the package, which is contained in the sdcMicro package available from the CRAN mirrors. The GUI, however, does not implement the full functionality of the sdcMicro package and is not discussed in this guide. The GUI can be called after loading sdcMicro by typing sdcApp() at the prompt.