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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.
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 ]
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."
The removal or generalization of 18 elements from the data. That the Covered Entity or Business Associate does not have actual knowledge that the residual information in the data could be used alone, or in combination with other information, to identify an individual. Safe Harbor is a highly prescriptive approach to de-identification.
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.
Business processes that handle personal data must be designed and built with consideration of the principles and provide safeguards to protect data (for example, using pseudonymization or full anonymization where appropriate). [33] Data controllers must design information systems with privacy in mind.
Pages in category "Data anonymization techniques" The following 6 pages are in this category, out of 6 total. ... Pseudonymization; R. Randomized response; U.
CWM models enable users to trace the lineage of data – CWM provides objects that describe where the data came from and when and how the data was created. Instances of the metamodel are exchanged via XML Metadata Interchange (XMI) documents. Initially, CWM contained a local definition for a data translation facility.