This is a community contribution and is written by Chike Okonkwo, co-founder of the web3 social media gaming protocol Gamic HQ.
The opinions and ideas expressed in this article are those of the contributor/author and do not necessarily reflect the views of Binance Academy.
Summary
Data tokenization refers to the process of converting sensitive data, such as credit card data, into tokens that can be securely transferred on a blockchain without revealing the original data.
Data encryption can enhance data security, privacy, and compliance and prevent any unauthorized access or misuse.
Data coding requires careful consideration and careful implementation to manage its advantages and disadvantages.
What is a token?
Tokens are non-minable digital units that exist as ledger entries in blockchain chains. They come in different forms and have multiple use cases. For example, they can be used as currencies or to encrypt data.
Tokens are generally issued using blockchains such as the Ethereum blockchain and the BNB blockchain, and popular token standards include ERC-20, ERC-721, ERC-1155, and BEP-20. Tokens are transferable units of value issued on top of a blockchain, but they are not digital currencies like Bitcoin or Ethereum that are native to the respective blockchain.
Some tokens may be exchangeable for off-chain assets such as gold and property. This procedure is called real asset tokenization.
What is data encoding?
Data tokenization refers to the process of converting sensitive data, such as credit card or health data, into tokens that can be transformed, stored, and manipulated without revealing the original data.
These tokens are typically unique, immutable, and verifiable on the blockchain to enhance data security, privacy, and compliance. For example, a credit card number can be encoded by turning it into a string of random numbers that can be used to verify payment without revealing the actual card number.
Data tokenization can also be applied to social media accounts, users can tokenize their online presence to move seamlessly from one social media platform to another while retaining ownership of their personal data.
The concept of data encryption has only been around for a while now, and its use is common in the financial sector to protect payment information but has potential application in many other fields.
What is the difference between coding and encryption?
Coding and encryption are two ways to protect data, but each method works differently and serves different purposes.
Encryption is the process of converting plaintext data into an unreadable form (ciphertext) so that it can only be decrypted with a secret key. It is a mathematical process that scrambles the data to make it unreadable to anyone who does not have the key. Encryption is used in various scenarios, including protecting communications, data storage, authentication, digital signatures, and regulatory compliance.
On the other hand, tokenization refers to the process of exchanging sensitive data with unique, non-sensitive identifiers called tokens, which does not rely on a secret key to protect the data. For example, a credit card number may be swapped with a token that has no relation to the original number but can still be used to process the transaction.
Tokenization is often used when data security and compliance with regulatory standards become important such as payment processing and management of healthcare information and personally identifiable information.
How does data encoding work?
Suppose a user wants to move from one social media platform to another. On traditional social media platforms of the second generation of the web, the user may have to create a new account and enter all his personal data from the beginning, and it is also possible that the history of existing posts and contacts will not be transferred From the old platform to the new platform.
Using data tokenization, users can link their existing digital identity to the new platform to automatically transfer their personal data. To do this, the user must have a digital wallet such as Metamask with the wallet address representing their on-chain identity.
The user can then link the wallet to the new social media platform. Personal history, contacts and assets are automatically synced on the new platform because Metamask includes the user's digital identity and data on the blockchain.
Which means that the user will not lose any tokens, non-tradable tokens (NFTs) and previous transactions made on the previous platform, and thus the user feels complete control over his choice of the platform to which he will move to, without being restricted to a specific platform.
Advantages of data encoding
Enhance data security
Data tokenization enhances data security. By exchanging sensitive data with tokens, data tokenization reduces the risk of data breaches, identity theft, and other cyberattacks. The tokens are linked to the original data with a secure mapping system so the original data remains protected even if the tokens are stolen or leaked.
Compliance with regulations
Many sectors are subject to strict regulations around data protection, and tokenization can help meet organizations' requirements to secure sensitive information and provide a solution to reduce the chances of non-compliance. Because tokenized data is considered non-sensitive, it can reduce the complexity of security audits and make it easier to manage data.
Secure data sharing
Tokenization enables secure data sharing between departments/departments, vendors, and partners By only providing access to tokens without revealing sensitive information, tokenization can efficiently scale to meet the growing needs of organizations while reducing the cost of implementing data security measures.
Data coding deficiencies
Data quality
Data tokenization may impact data quality and accuracy because some information may be lost or distorted during the encoding process. For example, if a user's location is tokenized, this may negatively impact their ability to see relevant content based on location.
Operational compatibility
Data encoding may make it difficult for different systems that use or process data to work with each other. For example, if a user's email address is encoded, the user may not receive notifications from some platforms or other services. If a user's phone number is encoded The user may not be able to make or receive calls or send or receive text messages, depending on the platforms he uses.
Data governance
Data tokenization may raise some legal and ethical questions about who owns and controls the data and how it is used and shared. Tokenizing a user's personal information, for example, could change the way they consent to how their data is collected and used. In another example, marking up social media posts may hinder Social networking deprives a user of his freedom of expression or deprives him of intellectual property rights.
Data recovery
Data recovery can be more complicated when the tokenization system fails, as organizations have to recover both the data converted to tokens and the original sensitive data stored in the token vault, which makes it complicated.
Data tokenization use cases: social media and non-fungible tokens (NFT)
Social media platforms collect vast amounts of user data every day to create targeted ads, filter content, and personalize user experiences, and this information is often stored in central databases that can be sold without users' permission or compromised.
Users can tokenize their social media data and sell it to advertisers or search parties, if they wish. Users can control who can see or share their content, and can also set custom rules for their profile and content.
For example, they can allow only verified users to see their content or set a minimum token balance for those they want to interact with, giving users full control over their social graph, content, and monetization channels like tipping and subscriptions.
Concluding thoughts
Data tokenization is already common in industries including healthcare, finance, media, and social media, and its prevalence is expected to expand due to the growing need for data security and regulatory compliance.
Successful implementation of this approach requires careful consideration and careful implementation, and data coding must be performed in a sound and responsible manner that respects users' rights and expectations while at the same time complying with all relevant laws and regulations.
Related articles:
How Web 3 is transforming sports, music and fashion
What are the token standards?
Introduction to ERC-20 tokens
How does artificial intelligence affect the NFT ecosystem?
Disclaimer and Risk Warning: This content is provided to you “as is” for general information and educational purposes only, without any representations or warranties of any kind. It should not be construed as financial advice, nor is it intended to recommend the purchase of any particular product or service. You should seek specialized advice from specialist consultants. If the article is submitted by a third party, please note that the number of views pertaining to that third party does not necessarily reflect the number of views on Binance Academy. Please read our full disclaimer here for more details. Digital assets may be subject to price fluctuations, and the value of your investment may increase or decrease and you may not get back the amount you invested. You are solely responsible for your investment decisions and Binance Academy is not responsible for any losses you may incur. This article should not be construed as financial, legal or professional advice. For more information, please see our Terms of Use and Risk Warning.
