Data tokenization

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 extrinsic or exploitable meaning or value. The token is a reference (i.e. identifier) that maps back to the sensitive data through a tokenization system Tokenization can provide several important benefits for securing sensitive customer data: Enhanced customer assurance —tokenization offers an additional layer of security for eCommerce websites, increasing... Increased security and protection from breaches —by using tokenization, businesses do not.

Tokenization (data security) - Wikipedi

Tokenization is the process of turning sensitive data into nonsensitive data called tokens that can be used in a database or internal system without bringing it into scope. Tokenization can be used to secure sensitive data by replacing the original data with an unrelated value of the same length and format To do this: Visit: https://console.cloud.google.com/dataflow Click on Create job from template Select Data Masking/Tokenization using Cloud DLP from Cloud Storage to BigQuer

Data tokenization's most powerful application is likely this mingling of tokenized government data with other data sources to generate powerful insights—securely and with little risk to privacy (figure 1). Apart from the ability to deidentify structured data, tokenization can even be used to deidentify and share unstructured data. As governments increasingly use such data, tokenization. Hence, Tokenization is the foremost step while modeling text data. Tokenization is performed on the corpus to obtain tokens. The following tokens are then used to prepare a vocabulary. Vocabulary refers to the set of unique tokens in the corpus Tokenization definition Tokenization is the process of turning a meaningful piece of data, such as an account number, into a random string of characters called a token that has no meaningful value if breached. Tokens serve as reference to the original data, but cannot be used to guess those values Data tokenization allows you to maintain control and compliance when moving to the cloud, big data, and outsourced environments. If the type of data being stored does not have this kind of structure - for example text files, PDFs, MP3s, etc., tokenization is not an appropriate form of pseudonymization Tokenization is the process of transforming a piece of data into a random string of characters called a token. It does not have direct meaningful value in relation to the original data. Tokens serve as a reference to the original data, but cannot be used to derive that data

What is Tokenization Data & Payment Tokenization

Data tokenization can be applied directly on application servers on which the software is installed, or API requests from application to web servers on which tokenization software is installed can be utilized for tokenization. A full range of predefined tokenization formats are provided with the distribution and customers can create additional formats Data tokenization is a method that transforms a data value to a token value and is often used for data security and compliance. There are basically three approaches to enabling data tokenization: There are basically three approaches to enabling data tokenization

What is Tokenization? Everything You Need to Know TokenE

1. Two of the more prevalent methods for data tokenization are a tokenization vault or service and vaultless tokenization. Tokenization vaults or services use either a database or file-based method that replaces the original data value with a token and stores the original plaintext value and the respective token inside a file or database
2. At its core is the concept of using a blend of advanced distributed ledger technologies to give human beings much more control over their own data. These technologies combine two foundational..
3. Tokenization is b reaking the raw text into small chunks. Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words
4. ate the up-front costs associated with implementing an on-premises... Improve.
5. Tokenization is the process of converting plaintext into a token value which does not reveal the sensitive data being tokenized. The token is of the same length and format as the plaintext, and that plaintext and token are stored in a secure token vault, if one is in use. One of the reasons tokenization is not used, however, is due to the process resulting in an undecipherable and irreversible token. Tokenization can be irreversible if a vaultless tokenization method is used

Tokenization is one of the most common tasks when it comes to working with text data. But what does the term 'tokenization' actually mean? Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms. Each of these smaller units are called tokens. Check out the below image to visualize this definition. Definition of Tokenization - Gartner Information Technology Glossary Tokenization refers to a process by which a piece of sensitive data, such as a credit card number, is replaced by a surrogate value known as a token Tokenization, in both senses of the term, plays a key part in EWF's open-source, blockchain-distributed energy data standardization and transaction systems development plans. In the first, most prominent use of tokenization, the Energy Web Token performs two main functions: to secure the network (as a deterrent to and safeguard against cyberattacks), and as a means of validator-node. Data tokenization is the process used to de-identify direct identifiers by replacing raw data with randomly generated tokens, or pseudonyms. The Privitar Data Privacy Platform™ supports tokenization with optional consistency, format preservation and reversibility. Enable Consistency & Linkabilit

Take charge of your data: How tokenization makes data

1. Tokenization Reduces Data Theft Risk The primary difference—and benefit—of using tokenization vs. encryption is that tokenized data cannot be returned to its original form. Unlike encryption, tokenization does not use keys to alter the original data
2. Tokenization is the art of substituting meaningless data to meaningful data. It's different from encryption in the sense that, to perform tokenization you build a dictionary where the token entries are mapped to the original entries. Without the knowledge of the dictionary - which acts as the key - there is no way to understand the nature and value of the tokenized data
3. Data tokenization replaces certain data with meaningless values. However, authorized users can connect the token to the original data. Token data can be used in production environments, for example, to execute financial transactions without the need to transmit a credit card number to an external processor. Three data obfuscation methods . Why is Data Obfuscation Important? Here are a few of.

Tokenization substitutes a sensitive identifier (e.g., a unique ID number or other PII) with a non-sensitive equivalent (i.e., a token) that has no extrinsic or exploitable meaning or value. These tokens are used in place of identifiers or PII to represent the user in a database or during transactions such as authentication Tokenization is a reversible protection mechanism. When applying tokenization, a sensitive data element is substituted by a so-called token. The token itself maps back to the original data element but doesn't expose any sensitive data. If playback doesn't begin shortly, try restarting your device Tokenization is a process of replacing sensitive data with non-sensitive data. In the payments industry, it is used to safeguard a card's PAN by replacing it with a unique string of numbers. How does tokenization work? The payment token itself is the unique string of numbers - a secure identifier generated from a PAN. Payment tokens are automatically issued in real-time and used online in. Tokenization is a data security feature where a sensitive data element or set is effectively replaced (tokenized) with a non-sensitive alternative, called a token. This renders the data completely useless to exploitation. Tokenization can be used to safeguard data in a number of areas such as medical records, banking and credit card payments Tokenization is the process of replacing sensitive data with non-sensitive data. For example: if a customer is going to pay using payment card, the payment card number should not be readable. If the payment card's number is xxxyyyzzzz, then using tokenization on this number would change it to apxcladajedpo9iiuwqdw

Data tokenization for government Deloitte Insight

Tokenization is a security technology that can be used with many kinds of sensitive data, not just payment card information. You may want to protect Social Security numbers, customer account. The Tokenization of Data and How Constellation is Forging a Path for New Financial Instruments Through Microservices. by Stardust Collective | Oct 5, 2020 | Blockchain, Microservices, Tokenized Data. Just as natural resources shaped the Industrial Revolution, big data is the driving force in the digital revolution-which has changed the ways we perceive, share and interact with that data. The.

What is Tokenization Tokenization In NL

1. Tokenization alternatives sensitive data with identical non-sensitive data. The nonsensitive, alternate data is named a token. Let's say you are purchasing something from a merchant that makes use of tokenization. If the tokenization system works well in that place, it impedes the data of your card and then replaces it with an unplanned string of letters and numbers. In place of Jane Smith.
2. Tokenization is a form of fine-grained data protection that replaces a clear value with a randomly generated synthetic value which stands in for the original as a 'token.' The pattern for the tokenized value is configurable and can retain the same format as the original which means less down-stream application changes, enhanced data sharing, and more meaningful testing and development with.
3. Data Tokenization. Merchants who maintain and manage customers' credit card data take on significant, costly, and ongoing PCI compliance responsibility and exposure. Tokenization is a process in which we store credit card data and personal information in our Customer Vault returning a secure token or alias to the merchant
4. The tokenization of data isn't limited to financial information, however. Hospitals implement it for patient records. Software programs utilize it to keep credentials secure. And some governments tokenize voter registration. Blockchain Tokenization Is Slightly Different. Whereas tokenization in the traditional sense revolves around data, blockchain tokenization focuses on assets.
5. A complete understanding of both Data Masking & Tokenization is vital to securing your business against potential security threats. Check out this blog for mor
6. keeping the data at all. Tokenization lets us remove sensitive data for some services and data stores while retaining most of its value. In this report we dig deep into tokenization to explain how the technology works, explore different use cases and deployment scenarios, and review selection criteria to pick the right option. We'll cover everything from tokenization services for payment.

What is Tokenization vs Encryption - Benefits Uses Cases

1. The various tokenization functions in-built into the nltk module itself and can be used in programs as shown below. Line Tokenization. In the below example we divide a given text into different lines by using the function sent_tokenize. import nltk sentence_data = The First sentence is about Python. The Second: about Django. You can learn Python,Django and Data Ananlysis here. nltk_tokens.
2. Subword tokenization learns to group tokens from the data itself, and generally maintains this nuance so that downstream models get the more meaningful space provided by word tokenization while still allowing the model to understand unknown words and misspellings by breaking them into subword tokens
3. Tokenization converts a data placeholder to a token placeholder, replacing sensitive elements with randomly generated data mapped one-to-one within the environment. The original information is no longer contained within the tokenized version; therefore, the token cannot be easily reversed back to the original sensitive data. When to use it. Tokens can be used in applications to replace highly.
4. Tokenization is a common task a data scientist comes across when working with text data. It consists of splitting an entire text into small units, also known as tokens. Most Natural Language Processing (NLP) projects have tokenization as the first step because it's the foundation for developing good models and helps better understand the text we have. Although tokenization in Python could be.
5. Most traditional Tokenization solutions require you to make copies of the data and store it on disk before the data is tokenized. With BDM we tokenize the data in memory, ensuring that no copies of your sensitive data are on disk. Secure Stateful and Stateless Tokenization algorithms, applied correctly with strict User Access policies, allow you control who can access your PII and Sensitive data
6. The tokenization and decentralization of data offers such an alternative. While the first generation of utility tokens were backed by nothing more than dreams, a new generation of tokens.
7. Credit card data portability and tokenization. First, we need to understand what data portability is and why it is important. Suppose you are a customer of an online store where on a previous visit you entered your credit card details. Since then, because the company has decided to optimize transaction processes or start operating in new countries, it has had to change payment gateways and.

Tokenization replaces sensitive data elements with non-sensitive elements with no exploitable value. In many cases, you can even perform analytics on tokenized data, eliminating the risk of exposing sensitive data during processing. Security travels with the data in house and in the cloud. Check out our solution brief to learn more: Related posts. Oct 16, 2020 l Tokenization, Data-Centric. Data Tokenization protects all your sensitive data by tokenizing what is captured. In practice, the data is replace by a token which you can then work with in the place of the original data. In the event of a breach, the cyber criminals will find meaningless tokens in the place of the sensitive data With practical applications ranging from streamlining supply chains to managing retail loyalty points programs, tokenization has enormous potential to simplify and accelerate complex business processes—while also making them more secure. Get this tokenization white paper to learn about: Core concepts and definitions of tokens Tokenization replaces plain text data with irreversible, non-sensitive placeholders (tokens). In contrast to encryption, tokenization does not use a key in conjunction with a mathematical algorithm to transform sensitive data. With tokenization, it is possible to ensure that the replaced data has the same length and data type as the original data. This is a clear advantage over data encryption. With tokenization, data are masked in ciphertext, making the data unidentifiable and useless to attackers. Tokenization is an approach to protect data at rest while preserving data type and length. It replaces the original sensitive data with randomly generated, nonsensitive substitute characters as placeholder data. These random characters, known as tokens, have no intrinsic value, but they.

Data analysts converge sentence tokenization output into numeric for the machine learning model feed. Hence, it plays a vital role in feature engineering while performing text analysis. You can also practice the sentence tokenization technique by using the below example Tokenization and Text Data Preparation with TensorFlow & Keras. This article will look at tokenizing and further preparing text data for feeding into a neural network using TensorFlow and Keras preprocessing tools. By Matthew Mayo, KDnuggets. In the past we have had a look at a general approach to preprocessing text data, which focused on. Imperva Data Masking pseudonymizes and anonymizes sensitive data via data masking so you can safely use de-identified data in development, testing, data warehouses, and analytical data stores. Categories in common with Data Tokenization, Masking and Transformation: Data Masking. Data De-identification and Pseudonymity

What is Tokenization? Thale

The tokenization process helps to reduce the scope of compliance audits because customer credit card numbers, for example, are exchanged for tokens as soon as they are captured at a point-of-sale terminal, after which that data is no longer in compliance scope because the data no longer contains actual credit card numbers. Data remains in tokenized form by default, so any system that cannot. The conventional tokenization mechanisms focused on data only, while the asset tokenization with blockchain brought the focus on assets. You can issue a blockchain token as a digital representation of any actual tradable asset. It allows you to trade with even a single fraction of the asset. Now, many beginners can confuse the process with fractional ownership or securitization. However. How data tokenization works. Tokenization replaces sensitive data with substitute values called tokens. Tokens are stored in a separate, encrypted token vault that maintains the relationship with the original data outside the production environment. When an application calls for the data, the token is mapped to the actual value in the vault outside the production environment. Tokens can. Tokenization is the process of replacing actual values with opaque values for data security purposes. Security-sensitive applications use tokenization to replace sensitive data such as personally identifiable information (PII) or protected health information (PHI) with tokens to reduce the security risks Protecting PII & Sensitive Data on S3 with Tokenization. Eran Levy; February 24, 2020; Data security is more important than ever: During 2019, there was an increase in cyber-security crimes involving data breaches, with the global average cost of a single data breach reaching a staggering $3.92 million (according to a study by the Security Intelligence institute). These numbers are only.

Building a serverless tokenization solution to mask

Data tokenization isn't only designed to keep anonymous criminals at bay. In fact, it is also designed to stop sensitive information from people who are connected with your company or your organization. That can include any stakeholder, such as your employees, your suppliers, or your vendors. You must understand that no one can read these randomly generated IDs cannot be read by anyone, but. Data tokenization solutions are the key to achieving both. Tokenization Solutions Are Making Card Payments Safer. Within the finance industry, the payment card sector comes with extensive government regulations and concerns over data security. However, tokenization solutions offer a means of protecting cardholder data, such as the primary account number and magnetic swipe information. This. When data is continually used for business purposes such as testing and development, encryption or tokenization becomes a complicated process. The user needs to use a key to decrypt the ciphertext. Tokenization will generally be one of the first steps when building a model or any kind of text analysis, so it is important to consider carefully what happens in this step of data preprocessing. As with most software, there is a trade-off between speed and customizability, as demonstrated in Section 2.6

Data tokenization is the process of replacing sensitive data with a token - a randomized string of numbers that acts as a placeholder for real information. During PDCflow's payment tokenization, we generate a unique, random token to be used in place of the card or bank account number through the rest of our system. This token has no relation to the original number, and as such, can. Adding our tokenization solution reduces merchant exposure to card data compromise and its effect on a merchant's reputation. It also provides a secure, cost-effective way to keep sensitive card details away from a merchant's system, which can reduce the scope of PCI-DSS [SC1] (Payment Card Industry Data Security Standard) requirements and its associated cost diverse tokenization platforms or token exchanges. • Platform integration Depending on the business model they choose to embrace, they will implement different operating models. One of the main components of those new operating models being the blockchain platform, they will have to choose which platforms they will work or collaborate with. This will depend on the regulation they have to. How tokenization works. Tokenization has been on the rise in the blockchain sector, in particular in 2021. But the concept of tokenization has been around for a while. As far back as the 1970s, tokenization was seen as a data security mechanism popularized by financial companies

CipherTrust Tokenization Thale

1. sensitive data, because tokenization occurs at the field level. This means the data is tokenized before it goes into the data base, which reduces the danger of insider-access and credential-theft breaches. Dynamic Data Masking Dynamic Data Masking is a technology that protects data by dynamically masking parts of a data field, and Thales's Vormetric Token Server can set up rules regarding.
2. External Tokenization allows organizations to tokenize sensitive data before loading that data into Snowflake and dynamically detokenize data at query runtime using masking policies with External Functions. External tokenization requires External Functions, which are included in the Snowflake Standard Edition, and you can use external functions.
3. Tokenization is a data security principle where a token with no intrinsic value is used to unlock access to data. Tokenizing an asset, meanwhile, is the process of breaking an asset into digital tokens. You might break a company's share, for example, into 100 different digital tokens. You've tokenized that share. You can sell the tokens, and each token represents 1/100th of that.
4. Tokenization is the process of turning a meaningful piece of sensitive data, such as a social security number, into a random string of characters (called a token) that have no meaningful value if.
5. Difference between Tokenization and Masking : It is a process of applying mask to a value. It is a process of replacing sensitive data with non-sensitive data. It simply ensures efficient use of masked data for analysis without fear of leaking private information. It simply ensures correct formatting and transmission of data thus making it less.
6. DevCentral's John Wagnon highlights a solution that tokenizes secure data like credit cards to keep that data from being directly handled by the application.
7. Serverless tokenization with Protegrity delivers data security with the performance that organizations need for sensitive data protection and on-demand scale. About Tokenization. Tokenization is a non-mathematical approach to protecting data while preserving its type, format, and length. Tokens appear similar to the original value and can keep.

Difference Between Tokenization and Masking One of the biggest concerns of organizations dealing with banking, insurance, retail, and manufacturing is data privacy because these companies collect large amounts of data about their customers. And these are not just any data but sensitive, private data, which when mined properly gives a lot of insights about their customers Tokenization Choices Vormetric Tokenization combines the scalability and availability benefits of a vaultless solution with business-centric options for protecting data: both format-preserving and random tokenization. Format-preserving tokenization enables data protection without changing database schemas and offers irreversible tokens. Random tokenization offers high performance, convenient. The default *Base Tokenization map is designed for use with Latin-1 encoded data, as are the alternative *Unicode Base Tokenization and *Unicode Character Pattern maps. If these maps are not suited to the character-encoding of the data, it is possible to create and use a new one to take account of, for example, multi-byte Unicode (hexadecimal) character references

Loading Data. Please wait, we are loading chart data. STPT-Kursdaten live. Der Standard Tokenization Protocol-Preis heute liegt bei . €0.030753 EUR mit einem 24-Stunden-Handelsvolumen von €2,303,046 EUR. Standard Tokenization Protocol ist in den letzten 24 Stunden um 9.10% gefallen. Das aktuelle CoinMarketCap-Ranking ist #463, mit einer Marktkapitalisierung von €34,536,949 EUR. Es. Tokenization converts a data placeholder to a token placeholder, replacing sensitive elements with randomly generated data mapped one-to-one within the environment. The original information is no longer contained within the tokenized version; therefore, the token cannot be easily reversed back to the original sensitive data. When to use it. Tokens can be used in applications to replace highly. Tokenization And Annotation. Modern NLP is fueled by supervised learning. We annotate documents to create training data for our ML models. When dealing with token classification tasks, also known as sequence labeling, it's crucial that our annotations align with the tokenization scheme or that we know how to align them downstream. LightTa 101: Pre-processing data: tokenization, stemming, and removal of stop words. Michael Allen natural language processing December 14, 2018 December 15, 2018 6 Minutes. Here we will look at three common pre-processing step sin natural language processing: 1) Tokenization: the process of segmenting text into words, clauses or sentences (here we will separate out words and remove punctuation). 2. Tokenization is a non-mathematical approach to protecting data at rest that replaces sensitive data with non-sensitive substitutes, referred to as tokens, which have no extrinsic or exploitable meaning or value. This process does not alter the type or length of data, which means it can be processed by legacy systems such as databases that may be sensitive to data length and type

Python - Word Tokenization. Word tokenization is the process of splitting a large sample of text into words. This is a requirement in natural language processing tasks where each word needs to be captured and subjected to further analysis like classifying and counting them for a particular sentiment etc. The Natural Language Tool kit (NLTK) is. Tokenization is a useful key step in solving an NLP program. As we need to handle the unstructured data first before we start with the modelling process. Tokenization comes handy as the first and the foremost step. Recommended Articles. This is a guide to Tokenization in Python. Here we discuss introduction to Tokenization in Python, methods.

Tokenization software allows you to store data in a third-party database. As a result, your organization isn't required to maintain the staff and resources needed to manage sensitive data How Tokenization Can be Used for Securing Payment Card Transactions and Data. Over the summer, representatives of the merchant community called upon all stakeholders in the payments industry to work together on establishing open and efficient standards to protect consumers and businesses in the United States against security threats Tokenization also brings operational benefits, especially in the analytics and data mining space. A token can be used as a unique identifier for the customer on any system across an enterprise. This is a powerful feature that can support complicated analytics processes while minimizing challenges associated with stringent local data privacy and payments security compliance HPE Secure Data offers an end to end method to secure the organization data. This tool shields data to its complete development cycle that is deprived of revealing live data to risk. It has database integrity features enabled and compliance reporting like PCI, DSS, HIPPA etc. Technology supported by HPE is DDM, Tokenization etc. URL: HPE Secure.

Tokenization has been a game changer in the data security sphere. Tokenization is the process of substitution of sensitive meaningful pieces of data, such as account number, to a non-sensitive and random string of characters, known as a 'Token'. A token has no meaningful value if breached and therefore, can be handled and used as the original dataset. Tokens are stored in a 'vault' or. Tokenization of physical assets is to ensure the binding of digital tokens to a certain physical asset. For example, it is possible to tokenize the property rights of the entire office center. Due to the possibility of fractional ownership, one token can be equated to one square meter of an office space. Therefore, renting business will transform because there would be a particular number of. BERT - Tokenization and Encoding. To use a pre-trained BERT model, we need to convert the input data into an appropriate format so that each sentence can be sent to the pre-trained model to obtain the corresponding embedding. This article introduces how this can be done using modules and functions available in Hugging Face's transformers. Leverage your data to create real value. Connect your data catalog and instantly create marketplaces for buying or selling your digital assets. Leverage Nevermined's network to solve your most complicated data engineering challenges and build value while you do

Is Tokenization a Good Way to Protect Your Data? - Baffl

• 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 extrinsic or exploitable meaning or value. The token is a reference (i.e. identifier) that maps back to the sensitive data through a tokenization system. The mapping from original data to a token uses methods which render.
• To collect raw card data, you need to be fully PCI DSS compliant. If you are fully PCI DSS compliant, you can either create your own token vault or store raw card data to offer faster checkout options. To use network tokens that are issued by card networks (like Visa and Mastercard) for your payments, refer to Network tokenization. Next steps. required Create and use a token. Create tokens and.
• d that tokenization does. When harnessed correctly, tokenization eli
• Tokenization, in the context of electronic data protection, is the process of substituting a surrogate value (or token) for a sensitive data value in a processing system. These surrogate values could be Reversible Tokens, which are able to be returned to their original data value, or Irreversible Tokens, which remain irreversible and cannot be reidentified. When using Reversible Tokens.

Data Tokenization is Insecure - Here Are Some Example

• Tokenization is the protection of data elements in a reversible and format preserving way. Tokenization techniques allow not only underlying data format preservation, but also prescription of the appea-rance of the protected element. This includes, for example, preserving the length of the underlying data or padding it to a desired length. Other options include staying within the same.
• Tokenization solutions can be broadly divided into two categories: security tokenization and payment tokenization. Security tokenization (also known as acquirer tokenization or non-payment tokenization) approaches have traditionally been used to protect cardholder data and personally identifiable information (PII) stored in merchant databases
• DATA TOKENIZATION . United States Patent Application 20180302380 . Kind Code: A1 . Abstract: A data-source computer provides message data, having associated id data, to be sent to a data-collection computer; produces a blinded id by blinding the id data using a nonce; sends the blinded id to a tokenization computer; and sends the nonce and the message data via a network for receipt by the data.
• Tokenization, like encryption, is a reversible process that replaces sensitive data with data that can't be used by unauthorized parties. While encryption uses algorithms to generate ciphertext from plaintext, tokenization replaces the original data with randomly-generated characters in the same format (token values). Relationships between the original values and token values are stored on a.
• Preprocessing data using tokenization. Tokenization is the process of dividing text into a set of meaningful pieces. These pieces are called tokens. For example, we can divide a chunk of text into words, or we can divide it into sentences. Depending on the task at hand, we can define our own conditions to divide the input text into meaningful.

Tokenization converts (real-word or virtual) asset rights into a unique digital representation - a token. While the basic concept of digitalization is not new, distributed ledger technology (DLT) like blockchain adds an additional dimension. The process of tokenization is promising in that it creates a bridge between real-world assets and their trading, storage and transfer in the digital. Add a special-case tokenization rule. This mechanism is also used to add custom tokenizer exceptions to the language data. See the usage guide on the languages data and tokenizer special cases for more details and examples. Example from spacy. attrs import ORTH, NORM case = [{ORTH: do}, {ORTH: n't, NORM: not}] tokenizer. add_special_case (don't, case) Name Description; string: The. Tokenization is a process of replacing sensitive information with tokens—random strings of characters. Tokens are used to represent cardholder's information, such as a 16-digit card number or bank account details during the payment process, so the data are passed through a payment gateway without the card details being exposed