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- Tokenization (data security) - Wikipedia
Tokenization, when applied to data security, is the process of substituting a sensitive data element with a non-sensitive equivalent, referred to as a token, that has no intrinsic or exploitable meaning or value The token is a reference (i e identifier) that maps back to the sensitive data through a tokenization system
- What is tokenization? | McKinsey
In this McKinsey Explainer, we look at what tokenization is, how it works, and why it's become a critical part of emerging blockchain technology
- What Is Tokenization? - IBM
In data security, tokenization is the process of converting sensitive data into a nonsensitive digital replacement, called a token, that maps back to the original Tokenization can help protect sensitive information For example, sensitive data can be mapped to a token and placed in a digital vault for secure storage
- Back To Basics: Tokenization Explained - Forbes
At its heart, tokenization is the process of converting rights to an asset into a digital token on a blockchain In simpler terms, it's about transforming assets into digital representations that
- What is Tokenization? - Blockchain Council
Tokenization is the process of transforming ownerships and rights of particular assets into a digital form By tokenization, you can transform indivisible assets into token forms For example, if you want to sell the famous painting Mona Lisa
- How Does Tokenization Work? Explained with Examples - Spiceworks
Tokenization is defined as the process of hiding the contents of a dataset by replacing sensitive or private elements with a series of non-sensitive, randomly generated elements (called a token) such that the link between the token values and real values cannot be reverse-engineered
- What is Tokenization - OpenText
Tokenization is a process by which PANs, PHI, PII, and other sensitive data elements are replaced by surrogate values, or tokens Tokenization is really a form of encryption, but the two terms are typically used differently
- Tokenizers in Language Models - MachineLearningMastery. com
Tokenization is a crucial preprocessing step in natural language processing (NLP) that converts raw text into tokens that can be processed by language models Modern language models use sophisticated tokenization algorithms to handle the complexity of human language In this article, we will explore common tokenization algorithms used in modern LLMs, their implementation, and how to use them
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