An Introduction to Disclosure Risk Assessment
Data from household surveys, needs assessments and other forms of microdata make up an increasingly significant volume of data in the humanitarian sector. This type of data is critical to determining the needs and perspectives of people affected by crises but it also presents unique risks. Understanding how to assess and manage the sensitivity of this data is essential to ensuring its safe, ethical and effective use in different response contexts.
Disclosure risk assessment at work
OCHA
Why It's Important
Most humanitarian organisations acknowledge the sensitivity of personal data such as names, biometric data, or ID numbers.
This data should be anonymised, as a matter of standard practice, before being shared. However, even after removing the direct identifiers, it may still be possible to re-identify respondents.
By combining different data points, it may be possible to re-identify individuals or disclose confidential information.
Humanitarians can apply Statistical Disclosure Control to microdata to help detect and reduce this type of risk.
During emergencies, microdata needs to be shared with partners as quickly and safely as possible.
Having processes and tools in place to consistently assess and reduce the disclosure risk of this data enables organizations to share data in a safe, ethical and effective way.
The Stages of Statistical Disclosure Control
Limiting the risk of disclosure using statistical disclosure control techniques has three distinct stages:
Through these three stages of statistical disclosure control, you assess the disclosure risk in your data and then take steps to limit that risk. Because applying disclosure control techniques will result in information loss, the final stage of the process involves quantifying that loss in order to strike a balance between utility and risk in your data.
Limiting the risk of disclosure using statistical disclosure control techniques has three distinct stages:
Through these three stages of statistical disclosure control, you assess the disclosure risk in your data and then take steps to limit that risk. Because applying disclosure control techniques will result in information loss, the final stage of the process involves quantifying that loss in order to strike a balance between utility and risk in your data.
General Questions
Microdata provides information on a set of variables for each respondent in a dataset, where respondents can be individuals, households or establishments. In humanitarian settings, this type of data is gathered through exercises such as a Multi-Sector Needs Assessment (MSNA), community feedback & perception surveys, and other forms of needs assessments, household surveys, or monitoring activities.
SDC techniques are intended to prevent identity and attribute disclosure. Identity disclosure occurs when it is possible to associate a known individual with a released data record. Attribute disclosure occurs when it is possible to determine some new characteristics of an individual based on the information available in the released data
SDC techniques are not intended to prevent a third type of disclosure – inferential disclosure. Inferential disclosure occurs when it is possible to determine the value of some characteristic of an individual more accurately with the released data than would otherwise have been possible.
Statistical Disclosure Control is a technique used in statistics to assess and limit the risk of re-identification. The first step in this process is a disclosure risk assessment. This tutorial covers the steps required to conduct that risk assessment.
Learn more about conducting a disclosure risk assessment
We offer a series of short instructional videos and guidance to help you assess the sensitivity of your microdata and take action to reduce the risk of re-identification.
See How It's Done