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  2. Pseudonymization - Wikipedia

    en.wikipedia.org/wiki/Pseudonymization

    The pseudonym allows tracking back of data to its origins, which distinguishes pseudonymization from anonymization, [9] where all person-related data that could allow backtracking has been purged. Pseudonymization is an issue in, for example, patient-related data that has to be passed on securely between clinical centers.

  3. Data anonymization - Wikipedia

    en.wikipedia.org/wiki/Data_anonymization

    De-anonymization is the reverse process in which anonymous data is cross-referenced with other data sources to re-identify the anonymous data source. [3] Generalization and perturbation are the two popular anonymization approaches for relational data. [ 4 ]

  4. De-identification - Wikipedia

    en.wikipedia.org/wiki/De-identification

    Anonymization refers to irreversibly severing a data set from the identity of the data contributor in a study to prevent any future re-identification, even by the study organizers under any condition. [10] [11] De-identification may also include preserving identifying information which can only be re-linked by a trusted party in certain situations.

  5. Category:Data anonymization techniques - Wikipedia

    en.wikipedia.org/wiki/Category:Data...

    Download as PDF; Printable version; ... Help. Category for data anonymization techniques Pages in category "Data anonymization techniques" ... Pseudonymization; R.

  6. Data masking - Wikipedia

    en.wikipedia.org/wiki/Data_masking

    Data masking or data obfuscation is the process of modifying sensitive data in such a way that it is of no or little value to unauthorized intruders while still being usable by software or authorized personnel. Data masking can also be referred as anonymization, or tokenization, depending on different context.

  7. Data re-identification - Wikipedia

    en.wikipedia.org/wiki/Data_re-identification

    Such data has proved to be very valuable for researchers, particularly in health care. GDPR-compliant pseudonymization seeks to reduce the risk of re-identification through the use of separately kept "additional information". The approach is based on an expert evaluation of a dataset to designate some identifiers as "direct" and some as "indirect."

  8. Datafly algorithm - Wikipedia

    en.wikipedia.org/wiki/Datafly_algorithm

    Datafly algorithm is an algorithm for providing anonymity in medical data. The algorithm was developed by Latanya Arvette Sweeney in 1997−98. [1] [2] Anonymization is achieved by automatically generalizing, substituting, inserting, and removing information as appropriate without losing many of the details found within the data.

  9. Data anonymisation - Wikipedia

    en.wikipedia.org/?title=Data_anonymisation&...

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