Statistical disclosure control

Statistical disclosure control (SDC), also known as statistical disclosure limitation (SDL) or disclosure avoidance, is a technique used in data-driven research to ensure no person or organization is identifiable from the results of an analysis of survey or administrative data, or in the release of microdata. The purpose of SDC is to protect the confidentiality of the respondents and subjects of the research.^[1]

SDC usually refers to 'output SDC'; ensuring that, for example, a published table or graph does not disclose confidential information about respondents. SDC can also describe protection methods applied to the data: for example, removing names and addresses, limiting extreme values, or swapping problematic observations. This is sometimes referred to as 'input SDC', but is more commonly called anonymization, de-identification, or microdata protection.

Textbooks (e.g. Statistical Disclosure Control^[2]) typically cover input SDC and tabular data protection (but not other parts of output SDC). This is because these two problems are of direct interest to statistical agencies who supported the development of the field.^[3] For analytical environments, output rules developed for statistical agencies were generally used until data managers began arguing for specific output SDC for research.^[4]

This page focuses on output SDC.