De-identification

De-identification is the process of removing or masking personally identifiable information (PII) in order to reduce the risk that subjects’ identities be connected with data. De-identification is a critical component of ethical human subjects research. This page will discuss how to handle and de-identify incoming PII data before cleaning, analyzing, or publishing data.

Read First

• In general, the research team should always work with and analyze de-identified data, except when planning follow-up data collection or monitoring data.
• Publicly released data or replication data shared with other researchers must always be carefully de-identified.
• To de-identify data, 1) drop PII variables not necessary for the analysis, then 2) de-identify PII variables necessary for the analysis by masking, encoding, and anonymizing. For more details on what constitutes PII, see Personally Identifiable Information (PII).

Data Flow

The following steps ensure proper handling and storage of PII:

1. Save the raw, identified data to the Survey Encrypted Data folder, housed in the Encrypted Round Folder. The data in this folder should be exactly as you got it: absolutely no changes should be made to it.
2. De-identify the data by dropping the PII variables not necessary for analysis and masking, coding, or anonymizing the PII variables necessary for the analysis. Make sure to create reproducible do-files for the de-identification process. Save these do-files in the Dofiles Import Folder, housed in the Encrypted Round Folder.
3. Save the de-identified data set in the De-identified Folder, housed in the DataSets Folder. This is the raw data set with which the research team will begin to work.

In general, the research team should only use the data in the Survey Encrypted Data folder to plan follow-up data collection or to monitor data quality. Otherwise, the research team should work with de-identified data in the DataSets Folder. If necessary, the research team can work with data sets containing PII for reasons outside of follow-ups and monitoring, given they take special measures to ensure that the data set is secure and protected. However, note that not all file sharing services facilitate secure sharing of encrypted files.

The remainder of this page details how to de-identify a dataset before saving it to the De-Identified Folder.

Dropping PII Not Necessary for Analysis

To begin de-identification, drop all PII variables not necessary for analysis. This may include household coordinates; birth dates; contact information; IP address; and/or the names of survey respondents, family members, employees, and enumerators. If the research team later needs this information for follow-up surveys, high-frequency checks, back-checks, or other monitoring, they should refer to the Survey Encrypted Data Folder. Otherwise, the data regularly handled by the research team should not include this information.

De-identifying PII Necessary for Analysis

Next, de-identify all PII necessary for analysis by masking or encoding variables. When choosing between methods of masking and encoding, researchers face a trade-off between ensuring data privacy and losing information and thus results quality: different methods alter regression results and inference in different ways. This section details methods and limitations.

Encoding Categorical Variables

Encoding is a process of de-identifying PII categorical variables needed for analysis (i.e. administrative units, ethnicity) by dropping the value label of a factor variable. The unlabeled data then indicates which individuals are in the same group, but not what the group is. When encoding categorical variables, avoid using pre-existing codes such as State codes used by the National Statistics Bureau or another authority, as this would no longer constitute de-identification. Instead, use anonymous IDs to encode variables.

Masking Continuous Variables

Masking is the process of limiting disclosure of continuous PII variables needed for analysis. Some of the most used methods, as well as their advantages and disadvantages, are discussed below. See Additional Resources for more detailed information on how to implement each of them.

• Categorization is the process of transforming continuous variables into categorical variables by reporting a variable range rather than its specific value. For example, a 22-year-old individual might be classified as “18 and 25 year old.” The range of each category will depend on how many individual observations exist in each of them.
• Micro-aggregation is the process of forming groups with a certain number of observations and substituting the individual values with the group mean. This method alters the variable variance and, accordingly, may affect estimation. However, the change in variance is small if the groups are small.
• Adding noise is the process of creating white noise by generating and adding to the original variable a new variable with mean zero and positive variance. This method alters the original variable’s variance, therefore affecting inference.
• Rounding is the process of defining, often randomly, a rounding base and rounding each observation to its nearest multiple.
• Top-coding is used when only a few extreme values can be individually identified. In this process, extremely high values are rounded so that, for example, any farmers producing more than a certain quantity of a crop are assigned that quantity.

When masking a variable, make sure to do so in a way that a third party could not reverse to uncover the true value. For example, if you dislocate every GPS coordinate two kilometers south, one could easily trace the value back to the original coordinates. Similarly, if you create one single noise variable with different values for each observation and add it to multiple variables to de-identify them, their original value can be obtained more easily than if you add different noises to different variables.

It is important to document any changes made to variables during de-identification so that researchers can take them into account when conducting analysis and interpreting results. Save this documentation in a secure, encrypted location.

Anonymizing Data

When a survey sample comes from a previously existing registry, or when survey data needs to be matched to administrative data, it is common to use a pre-existing ID variable from the same registry or database, for example, State codes or clinic registries. Since people outside of the research team have access to these IDs, there is no way to guarantee protection or privacy of the collected data. In such cases, it is a best practice to create a new ID variable with no association to the external ID. There are however some exceptions to this general rule.

Statistical Disclosure Control and sdcMicro

Another important aspect of dealing with statistical data is statistical disclosure control (SDC). The concept of SDC seeks to modify and treat the data in such a way that the data can be published or released without revealing the confidential information it contains. At the same time, SDC tries to limit information loss from the data anonymization.

In this regard, Matthias Templ, Alexander Kowarik and Bernhard Meindl have created the sdcmicro package in R. This package can be used for generating anonymized microdata, that is, data for public and scientific use. To install sdcmicro in R, run the following command:

library(sdcMicro) # loading the sdcMicro package
require(sdcMicro) # loading the sdcMicro package

Further, as part of its efforts to help researchers with anonymizing data, the International Household Survey Network (IHSN) has released the following resources:

• A practice guide for sdcmicro,
• An introduction to the theory of SDC, and
• A guide to using the graphic user interface (GUI) for sdcmicro.

Related Pages

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Additional Resources

• DIME Analytics (World Bank), Protect Privacy and Share Data Securely
• DIME Analytics (World Bank), Encryption 101
• Institute of Education Sciences (IES), Guidelines for protecting personally identifiable information (PII)
• J-PAL, Guide to De-identifying Data
• Ori Heffetz and Katrina Ligett, Privacy and Data Based Research
• Thijs Benschop and Matthew Welch, [http://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S1_2_IO_Benschop_Welch_P.pdf A Practice Guide for Microdata Anonymization}