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4 Types of Data Anonymization

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Data anonymization is the process of removing personally identifiable information from data. It is done in order to release information in such a way that the privacy of individuals is maintained. For example, census data might be released for the purposes of research and public disclosure with all names, postal codes and other identifiable data removed. The following are common types of data anonymization.

Removal

Completely removing fields that could be used in any way to identify a person. Considered a strong form of data anonymization.

Redaction

Redaction includes removal and other techniques such as blacking data out on paper with a marker and making a photocopy of the result.

Encryption

Encryption can be very strong and difficult to reverse. It does present a few challenges such as the generation of a sufficiently strong decryption key. Data anonymization isn't intended to be reversible so the management of decryption keys is also a concern. Ideally, a strong and fully random key would be generated and then immediately erased from memory when encryption completes.

Data Masking

Data masking is a potentially weak form of data anonymization that may include data scrambling and character replacement. The advantage of data masking is that it maintains the structure of data such that numbers remain numbers and dates remain dates. This allows anonymized data to be used for system testing without triggering application errors.
Overview: Data Anonymization
Type
Definition
The process of removing personally identifiable information from data.
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