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Pseudonymization is a data management and de-identification procedure by which personally identifiable information fields within a data record are replaced by one or more artificial identifiers, or pseudonyms. [1]
Location data - series of geographical positions in time that describe a person's whereabouts and movements - is a class of personal data that is specifically hard to keep anonymous. Location shows recurring visits to frequently attended places of everyday life such as home, workplace, shopping, healthcare or specific spare-time patterns. [ 14 ]
A pseudonym (/ ˈ sj uː d ə n ɪ m /; from Ancient Greek ψευδώνυμος (pseudṓnumos) 'lit. falsely named') or alias (/ ˈ eɪ l i. ə s /) is a fictitious name that a person assumes for a particular purpose, which differs from their original or true meaning ().
Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of removing personally identifiable information from ...
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.
The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing. I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. I2. (Meta)data use vocabularies that follow FAIR principles I3.
Data sanitization policy must be comprehensive and include data levels and correlating sanitization methods. Any data sanitization policy created must be comprehensive and include all forms of media to include soft- and hard-copy data. Categories of data should also be defined so that appropriate sanitization levels will be defined under a ...
Data verification helps to determine whether data was accurately translated when data is transferred from one source to another, is complete, and supports processes in the new system. During verification, there may be a need for a parallel run of both systems to identify areas of disparity and forestall erroneous data loss .