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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.
In computer programming, an anonymous function (function literal, expression or block) is a function definition that is not bound to an identifier.Anonymous functions are often arguments being passed to higher-order functions or used for constructing the result of a higher-order function that needs to return a function. [1]
Lexical tokenization is related to the type of tokenization used in large language models (LLMs) but with two differences. First, lexical tokenization is usually based on a lexical grammar, whereas LLM tokenizers are usually probability-based. Second, LLM tokenizers perform a second step that converts the tokens into numerical values.
A snippet of C code which prints "Hello, World!". The syntax of the C programming language is the set of rules governing writing of software in C. It is designed to allow for programs that are extremely terse, have a close relationship with the resulting object code, and yet provide relatively high-level data abstraction.
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 .
The C language provides basic arithmetic types, such as integer and real number types, and syntax to build array and compound types. Headers for the C standard library , to be used via include directives , contain definitions of support types, that have additional properties, such as providing storage with an exact size, independent of the ...
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.