Which statement best describes the primary purpose of de-identification in data sharing?

Which statement best describes the primary purpose of de-identification in data sharing?

• A. Improve data accuracy.
• B. Protect privacy while enabling data sharing and research.
• C. Ensure data cannot be used for research.
• D. Increase data granularity.

Answer: (B)

De-identification in data sharing is about protecting individuals' privacy while allowing researchers to use the data. It works by removing or masking information that could link records to specific people, so the data can be shared and analyzed without exposing identities. This often involves stripping direct identifiers like names or addresses and may include techniques such as pseudonymization, generalizing certain details, or suppressing rare values to reduce re-identification risk. The goal is to keep the data useful for research while lowering the chance that someone could be identified. Thinking about the options, the main idea isn’t to improve data accuracy—that can actually be affected when details are obfuscated to protect privacy. It also isn’t to prevent data from being used in research; de-identification is specifically designed to enable safe research use. And it isn’t about increasing the level of detail; it typically reduces granularity to minimize privacy risks.

De-identification in data sharing is about protecting individuals' privacy while allowing researchers to use the data. It works by removing or masking information that could link records to specific people, so the data can be shared and analyzed without exposing identities. This often involves stripping direct identifiers like names or addresses and may include techniques such as pseudonymization, generalizing certain details, or suppressing rare values to reduce re-identification risk. The goal is to keep the data useful for research while lowering the chance that someone could be identified.

Thinking about the options, the main idea isn’t to improve data accuracy—that can actually be affected when details are obfuscated to protect privacy. It also isn’t to prevent data from being used in research; de-identification is specifically designed to enable safe research use. And it isn’t about increasing the level of detail; it typically reduces granularity to minimize privacy risks.