Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of either encrypting or removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous.
Data anonymization has been defined as "technology that converts clear text data into a nonhuman readable and irreversible form, including preimage resistant hashes (e.g., one-way hashes) and encryption techniques in which the decryption key has been discarded." Data anonymization enables the transfer of information across a boundary, such as between two departments within an agency or between two agencies, while reducing the risk of unintended disclosure, and in certain environments in a manner that enables evaluation and analytics post-anonymization.
In the context of medical data, anonymized data refers to data from which the patient cannot be identified by the recipient of the information. The name, address, and full post code must be removed, together with any other information which, in conjunction with other data held by or disclosed to the recipient, could identify the patient.
De-anonymization is the reverse process in which anonymous data is cross-referenced with other data sources to re-identify the anonymous data source. Generalization and perturbation are the two popular anonymization approaches for relational data.
- Differential privacy
- Fillet (redaction)
- Masking and unmasking by intelligence agencies
- Statistical disclosure control
- Privacy Technology Focus Group Report (PDF). United States Department of Justice. 2006. p. 52.
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- "De-anonymization". Whatis.com. Retrieved 17 January 2014.
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