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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 ]
Data re-identification or de-anonymization is the practice of matching anonymous data (also known as de-identified data) with publicly available information, or auxiliary data, in order to discover the person to whom the data belongs. [1]
Data masking can also be referred as anonymization, or tokenization, depending on different context. The main reason to mask data is to protect information that is classified as personally identifiable information, or mission critical data. However, the data must remain usable for the purposes of undertaking valid test cycles.
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.
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l-diversity, also written as ℓ-diversity, is a form of group based anonymization that is used to preserve privacy in data sets by reducing the granularity of a data representation. This reduction is a trade off that results in some loss of effectiveness of data management or mining algorithms in order to gain
Pseudonymized data can be restored to its original state with the addition of information which allows individuals to be re-identified. In contrast, anonymization is intended to prevent re-identification of individuals within the dataset.