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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.
The tokenization system must be secured and validated using security best practices [6] applicable to sensitive data protection, secure storage, audit, authentication and authorization. The tokenization system provides data processing applications with the authority and interfaces to request tokens, or detokenize back to sensitive data.
Data anonymization is a type of information sanitization whose intent is privacy protection. It is the process of removing personally identifiable information from data sets , so that the people whom the data describe remain anonymous .
Tokenization has helped secure business data over the years, and now it is expanding the ability to trade ownership of assets all over the world. However, there are still many obstacles that are ...
A principal, though fundamentally theoretical, overview of terminology and principal anonymization technology is found in Pfitzmann & Hansen's terminology of anonymity. [7] In 1997, a report by Goldberg, Wagner and Brewer at the University of California, Berkeley summarized PETs. [8]
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.
Tokenization may refer to: Tokenization (lexical analysis) in language processing; Tokenization in search engine indexing; Tokenization (data security) in the field ...
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