Personally Identifiable Information (PII)

In the context of a survey, personally identifiable information (PII) are variables that can, either on their own or in combination with other variables, be used to identify a single surveyed individual with reasonable certainty. During all steps of research and field work, research teams must protect PII through encryption and de-identification. This page will explain how to identify PII and how to calculate its disclosure risk. For information on how to encrypt and de-identify datasets, see Encryption and De-identification.

Read First

• All PII must be stored in an encrypted folder.
• PII should be masked, encoded, or removed from the working dataset and any shared or published datasets. See de-identification for details on how to de-identify data.
• No PII can ever be publicly released without explicit consent. Researchers must ensure that this data remains private and safely stored.

Personally Identifiable Information

Common PII variables include:

• Names of survey respondent, household members, enumerators and other individuals
• Names of schools, clinics, villages and/or other administrative units (depending on the survey)
• Date of birth
• GPS coordinates
• Contact information
• Record identifier (i.e. social security number, process number, medical record number, national clinic code, license plate, IP address)
• Pictures of individuals or houses

Depending on survey context, the following variables may also be PII:

• Age
• Gender
• Ethnicity
• Grades, salary, job position

These lists aren’t exhaustive: what exactly is PII depends on the context of each survey. For example, if a survey covers a small farming community, variables such as plot size and crops cultivated could be combined to identify an individual household and, as such, would be PII. Administrative units could also be considered PII if there are few individuals in each of them.

Disclosure Risk

Details on how to calculate the disclosure risk – that is, the risk of someone being able to track individual respondents from the available data – can be found in Additional Resources. In order to calculate disclosure risk, researchers typically define a minimum threshold of individuals for which a certain value of the variable must apply in order for the variable be considered safe to disclose. If the threshold is not met, then the variable is considered PII. For example, at a threshold of 10, if a school has less than 10 students of a certain age, then age is considered PII as it could be used with other information to identify these students. The value of these thresholds depends on the context of the survey.

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This article is part of the topics Data Cleaning and Publishing Data.

Additional Resources

• Matthew and Harel's Data confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacy
• Shlomo's Releasing Microdata: Disclosure Risk Estimation, Data Masking and Assessing Utility
• DIME Analytics’ Research Ethics & Data Security
• DIME Analytics' slides on Encryption