Search results
Results From The WOW.Com Content Network
Tokenization may be used to safeguard sensitive data involving, for example, bank accounts, financial statements, medical records, criminal records, driver's licenses, loan applications, stock trades, voter registrations, and other types of personally identifiable information (PII). Tokenization is often used in credit card processing.
This is a type of tokenization used for payment processing that doesn’t require a token vault for storage. Instead, it uses cryptographic devices and algorithms to convert data to a token.
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.
We explore what tokenization is, how it works, and how it's revolutionizing the way assets can be issued, managed, and traded.
Tokenization may refer to: Tokenization (lexical analysis) in language processing; Tokenization in search engine indexing; Tokenization (data security) in the field ...
Tokenization: This process securely exchanges data online by converting it into tokens, ... You may be able to use a linked account as overdraft protection and avoid incurring an overdraft fee.
Biometric tokenization like its non-biometric counterpart, tokenization, utilizes end-to-end encryption to safeguard data in transit.With biometric tokenization, a user initiates his or her authentication first by accessing or unlocking biometrics such as fingerprint recognition, facial recognition system, speech recognition, iris recognition or retinal scan, or combination of these biometric ...
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.