What is Data Tokenization and Why is it Important?

Introduction

The prospect of a data breach is one of the most widely recognized concerns expressed by individuals and enterprises worldwide. In today’s digital age, our increased reliance on technology and data-driven processes propels us to prioritize the security of our data as one of our most important assets. According to a report by Statista, in America, over 422 million individuals were affected by data compromises such as breaches, leakages, and exposures in 2022. One security measure that has become widespread due to its scalability, cost efficiency, and security is data tokenization.

What is Data Tokenization?

Data tokenization is a highly effective data protection method that guards against data breaches. It achieves this by primarily restricting access to sensitive data such as a primary account number (PAN), personally identifiable information (PII), and other confidential information and replacing it with randomly generated strings of alphanumeric symbols without correlations to the original data.

This involves pulling up strings of unrelated unique alphanumeric symbols known as tokens to mask sensitive data such as credit card numbers, healthcare records, personal identification numbers (PINs), etc. While tokenized data bears no direct resemblance to the original data, it can share similar features such as length and character set.

Tokenization switches sensitive data in the data repositories to non-sensitive data that is random and has no real value. The original sensitive data is often stored in a centralized vault. Companies adopting this method can protect their consumers, build their confidence, and remain compliant with data privacy regulations.

Additionally, data tokenization has over time evolved from being concentrated in just healthcare and financial services to being used by enterprises in different sectors like E-commerce, telecommunications, social media, retail, etc. to protect the privacy of their customers and comply with regulations.

However, data tokenization shines brightly in the financial services industry; it’s the reason you can securely make payments without divulging sensitive data. That is possible because tokens are generated to replace your primary account number (PAN) and other banking details, with tokens acting as surrogates. These tokens provide an extra layer of security, making it incredibly difficult for anyone to reverse-engineer the original information from the tokens themselves.

When you throw blockchain into the mix, it works a bit differently. First, It is important to establish what tokens are. Tokens represent anything of value; they are used to digitize various real-world assets like real estate, Jewelry, art, you name it—just about anything that has real-world value can be tokenized on the blockchain. This tokenization process allows these assets to be recorded, transferred, and traded securely on the blockchain.

In the context of blockchain, data tokenization is the process of transforming data sets into unique tokens for the protection of sensitive personal information, and this sensitive personal information is tokenized to ensure that only authorized parties can access and use the data while keeping the original data secure. Blockchain technology revolutionizes tokenization by providing a robust and efficient method to represent, trade, and manage real-world assets and data securely and transparently.

How Does Data Tokenization Work?

Source: imiblockchain.com

We have established what data tokenization is, but how does it really work? Data tokenization starts with identifying the sensitive data that needs to be protected; it could be credit card numbers, social security numbers, etc. When the tokenization request is activated, the system randomly generates a surrogate token with no intrinsic value to replace the original data. Once the token is generated, it can be stored in a database or transmitted across networks.

In the Tokenization system, there is an option for mapping. Mapping makes it possible to create a link between the token and the original data, ensuring the possibility of the system retrieving the original data when needed.

In a more practical illustration of the tokenization process, James orders pizza online from McDonald’s. The website is equipped with tokenization, a robust data protection method. When James provides his credit card details, the website immediately initiates the tokenization process.

A unique token representing James’ card details is then generated and sent to his acquiring bank. To ensure the tokens’ authenticity, the bank collaborates with a Tokenization service provider, verifying that the tokens match James’s credit card. If a data breach occurs on McDonald’s servers, only useless tokens will be discovered thanks to data tokenization.

Methods of Data Tokenization

There are different processes for implementing tokenization, each with different use cases. The choice of a specific tokenization method depends on the specific requirements, security conditions, scalability, and level of data protection needed for a particular application. For the purpose of this article, we will be focusing on the following methods:

• Tokenization vault
• Vaultless tokenization

Tokenization Vault

This is one of the earliest methods that were Introduced. It is not a pre-generated data security process and works with a vault. Some enterprises keep a Tokenization vault that acts as a database that stores mappings between sensitive data and its corresponding token. As new data is collected, a new entry is added to the vault, and a token is generated. The vault keeps growing as more data is tokenized; this process is called On-Demand Random Assignment-based Tokenization (ODRA).

When the original data needs to be retrieved, a process known as de-tokenization is performed using the corresponding token, and the system looks it up to create a connection with the original data in the vault. This process, however secure it is, has an unavoidable downside when working with larger databases; its complexity threatens their ease of use, making it cumbersome to manage. That is where vaultless tokenization comes in.

Vaultless Tokenization

Vaultless Tokenization is designed to correct the complexities of ODRA by averting the use of vaults, a process known as stateless. There is no central vault to store mappings. Two commonly referred-to methods of vaultless Tokenization are: “static table-based Tokenization” and “encryption-based tokenization.” These methods do not depend on checking the vault or databases to find original data; they can directly derive the original data from the token using an algorithm, eliminating the long process of using a vault. This process is known to be a lot more efficient and easier to manage.

What are the Benefits of Data Tokenization?

Data tokenization offers undisputed benefits to individuals and enterprises. A dive into the benefits of data tokenization reveals the following:

Enhanced Security

Data Tokenization provides unique security benefits by masking sensitive data and leaving a decoy known as tokens to prevent bad actors from breaking into the sensitive data of users. For example, instead of using a customer’s 16-digit credit card number, you can substitute it for 16 strings of letters, symbols, or digits, making transactions safer and giving the customer increased trust.

Compliance with Data Regulations

Regulations require organizations to minimize employee access to raw data. Payment Card Industry Data Security Standard (PCI DSS) is one of the payment regulatory bodies, and non-compliance with GDPR and other regulations can amount to fines and sanctions by regulators. Tokenization helps mitigate the risk of non-compliance.

Anonymity and Privacy Protection

By pseudonymizing sensitive data, companies can adhere to the General Data Protection Regulation (GDPR) and protect the privacy of their users.

Fast and Efficient Transactions

Data tokenization allows for easier and more efficient data handling. Since tokenization does not rely on centralized databases like vaults, it can accommodate growing data, ensuring increased speed and efficiency while also upholding the integrity and security of the data, making it a suitable security solution for various industries.

Data Tokenization Vs. Encryption Vs. Hashing

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Data Tokenization

Data tokenization replaces sensitive data with non-sensitive, randomly generated symbols, text, etc. known as tokens, to prevent data exploitation. The only party that can associate the token with the sensitive data is the tokenization service provider. Encryption, on the other hand, uses algorithms to convert plaintext information into ciphertext (an unreadable form of text) and requires a secret key to decrypt the text into a readable form.

Data Encryption

Encryption uses algorithms to convert plaintext information into ciphertext (an unreadable form of text) and requires a secret key to decrypt the text into a readable form.

Source: Okta.com — Hashing Vs. Encryption

Data Hashing

Data hashing offers a solid security system based on a one-way cryptographic hash function. It allows sensitive data to be tokenized; however, reverse engineering these tokens to their original data is impossible. This feature makes hashing the ideal security measure for password storage, digital signatures, etc. because it hedges against data breaches.

Additionally, the table below summarizes the key distinctions between these three data protection mechanisms: tokenization, encryption, and hashing.

Differences between Data Tokenization, Encryption, and Hashing

Conclusion

Quoting Tim Berbers-Lee, who emphasized the importance of data, he said, “Data is a precious thing and will last longer than the systems themselves.” Safeguarding our data, therefore, becomes a pivotal task, as the benefits are boundless. Apart from instilling confidence in business processes, increasing customer trust, and being compliant with regulators, the reversibility of tokenization preserves the usability of information and makes managing data less cumbersome. To maintain the integrity of data and ensure data compliance, embracing tokenization is a necessity.