Machine Learning and Blockchain Technology: How do they work together?

A trend in recent years has been the use of blockchain technology. In this technology, individuals can deal directly with one another through a highly secure and decentralized system, without having to go through an intermediary. Furthermore, machine learning can be used to overcome many of the limitations of blockchain-based systems. When these two technologies are combined (Machine Learning and Blockchain Technology), excellent results can be achieved. We will investigate how machine learning capabilities can be integrated with a blockchain-based system in this article. This integrated approach will also be examined through examples and use cases. 

Technology based on blockchain 

Basically, blockchain technology is designed to decentralize the storage of data, so that it cannot be owned or managed by a single party. A transaction sheet can be used to update the information where, once noted in the sheet, it can not be modified. The upcoming transaction is then verified by a third party before it is entered into the sheet. The only difference is that the new set of records is checked by nodes on the decentralized network. This eliminates the need for centralized verification. 

Blockchain technology is a complex technology that consists of a number of blocks linked together that keep the flow of data going. A block’s hash is determined by the hash of the block before it, and so on. The blockchain mechanism makes it possible to trace data and transactions using this type of system. In contrast, they are resistant to change as the older blockchain cannot be changed, but yet changes are still made to the block, meaning that their hash is affected. 

There are three important components in blockchains:

  • Blocks: A blockchain is composed of many blocks, and every block is made up of three main elements:
  1. Data
  2. Null, a whole 32-bit number. Hash values are randomly generated when a block is generated, which results in the generation of block header hashes
  3. The hash is a 256-bit number that is very small and is connected to the nonce

Nonce distributes cryptographic hashes that are signed and tied to the blocks in a chain each time a block is created. Using the block data to mine, the nonce and hash are untied from the data.

  • Miners: A process known as mining is responsible for creating new blocks on the chain.

Every block contains a unique nonce and hash, and the hash in the present block refers to the hash of the previous block in the chain, which makes mining blocks difficult, especially for large chains.

A miner needs specialized skills to solve the complex mathematics involved in finding a nonce that generates an accepted hash. Nonces are only 32 bits, and hashes are 256 bits, so there are roughly billions of combinations of nonces and hashes that must be mined until the right combination is found. A “golden nonce” is a combination that allows a block to be added to the chain by a miner with the right combination. 

A golden nonce requires a tremendous amount of computing power and time. Blocks are hard to change, so blockchain technology is resistant to changes.

  • Nodes: As we have discussed, it is important to decentralize data in different blocks in order to make blockchain work. The data cannot be owned by one person. As a result, the chain can be owned by different people or groups. A node is regarded as a device that holds a copy of the blockchain and makes the chain or network function. 

All nodes own a copy of the blockchain, and the network is set to approve any newly mined block as trusted, updated, and verified. Because blockchains are transparent, it’s not easy to view or check each transaction. There are no duplicate transactions in the chain since each participant has their own unique identification number.

Based on its structure, the figure below shows how a blockchain is traceable and resistant to change.

Blockchain technology can be used for several different applications, some of which are listed below:

  • Trading of secure data
  • for international money transfers
  • IoT operating system for real-time data exchange
  • in logistics and supply chains
  • that uses cryptography
  • to protect personal information

Blockchain-based applications using machine learning

The learning capabilities of machine learning algorithms are amazing. In order to make the blockchain smarter than it was in the past, these capabilities can be applied. The integration of this technology can significantly enhance the security of the distributed ledger of the blockchain. Additionally, the computation power of ML can be used to reduce the time required to find the golden nonce and also to improve data sharing routes. The decentralized data architecture of blockchain technology allows us to build many better models of machine learning.  

For the purpose of making predictions or analyzing data, machine learning models may use the data stored in the blockchain network. As an example, let us take any BT-based application where the data is collected by sensors, smart devices, and IoT devices, and the blockchain is part of the application where machine learning model can be applied for real-time data analytics or predictions. It is said that storing the data in a blockchain network reduces the errors of machine learning models because the data in the network will not have missing values, duplicates, or noise, which is a primary requirement of a machine learning model for it to be accurate. In the image below, you can see an architecture for machine learning adaptation for a BT-based application.

Blockchain-based applications benefit from machine learning integration

Machine learning models have many benefits for blockchain technology, some of which are listed below:

  • When authorized users make changes to the blockchain, authentication is easy.
  • Our goal is to make BT a trusted and secure provider using machine learning.
  •  The integration of ML models can ensure the sustainability of terms and conditions previously agreed upon.
  • A ML model can be made based on the chain environment of BT.
  • Data models can help users extract good data. We can give rewards to users based on the results of the continuous computation 
  • The traceability of the BT allows us to also evaluate the hardware of different machines in order to prevent ML models from diverging from the learning paths that are assigned to them in the environment.
  • In the blockchain environment, we can implement a real-time trustworthy payment process.

Integrated Machine Learning and Blockchain Systems

A blockchain integrated system and machine learning can be used in a variety of ways. Listed below are some examples:

  • Customer service enhancement: As we all know, customer satisfaction is the primary objective of any company that is servicing its customers. Using a machine learning model or an AutoML framework on a Blockchain-based application, we can create a more efficient and automated service. 
  • ML models: By using ML models in the blockchain, companies can use blockchain for data trading around the world and make the process faster. ML models are used to manage trading routes for the data. Alternatively, we can also use them to validate data and encrypt it. 
  • Surveillance: Currently, most of the large manufacturing units and organizations are using blockchain-based procedures to enhance production, security, transparency, and compliance. ML algorithms can be used to make flexible maintenance plans during specific periods of time. Instead, the integration of machine learning can make quality control and product testing more automated.
  • Smart cities: Smart cities these days are helping in improving the living standards of the citizens. Machine learning and blockchain technologies play a crucial role in making smart cities. For example, smart homes can be monitored by machine learning algorithms while device personalization based on blockchain technology can improve the quality of living.
  • Security: With the increasing crime rate in the present scenario, security is an important concern of the people. It is possible to use both ML and BT for surveillance, where BT can be used for managing continuous data and ML can be used for analyzing the data.

Case Studies of Machine Learning and Blockchain Technology

  • As of today, there are various big and small companies that have either integrated both techniques together or integrated them into a system that provides a single output by integrating them. The following are a few examples of how blockchain technology can be used in machine learning:
  • A blockchain-based microfinancing strategy was launched by IBM in collaboration with Twiga Foods. Some machine learning techniques have been successfully applied in this project. ML techniques are employed to determine credit scores and predict the creditworthiness of different users based on blockchain data purchased from a mobile device. The lending and repayment process is then facilitated by blockchain technology.
  • The popular car manufacturer Porsche is one of those early adopters of technology that integrates ML and BT to improve vehicle capabilities and safety. By facilitating the process of parking, charging, and temporary access to their cars through third parties, the company offers its users peace of mind.
  •  Also in New York, a blockchain-based innovation enables local communities to generate and trade energy. For tracking and managing energy transactions, the technology uses microgrid smart meters and smart contracts based on blockchain.
  • In the food industry, other firms like Unilever and Nestlé are using blockchain and machine learning to deal with food disasters such as food waste and contamination to maintain their supply chains.

Final thoughts

We discussed the components and applications of blockchain technology in the article. Then, we explored how block technology could be combined with machine learning. Both these systems can be used together to cover up their shortcomings due to the benefits and applications of this integration. Here we covered a variety of uses for their integration as well as several applications.

Written by IOI

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