Book Description

A blockchain is a distributed public ledger that records transactions in a series of linked blocks that can be accessed by anyone. Before being added to the chain, the data (block) is time-stamped and verified. Each block builds upon the data in the one before it. The data is very difficult to forge due to the mathematical complexity of the storage system. The legacy of cryptocurrencies has helped turn the cryptographic term "blockchain" into a trendy catchphrase. A lot of people think blockchain is the same thing as cryptocurrencies. The opposite is true. Blockchain is the underlying technology behind cryptocurrencies, but its uses extend well beyond that. Blockchains may be considered for use in situations requiring the validation, auditing, or exchange of data. Here, we survey the literature on integrating blockchain with machine learning, and show that the two may work together successfully and efficiently. Machine learning is an umbrella word that includes a wide range of techniques, such as traditional ML, DL, and RL. As a distributed and append-only ledger system, the blockchain is a natural instrument for sharing and processing large data from multiple sources thanks to the inclusion of smart contracts, which is a crucial component of the infrastructure necessary for big data analysis. When it comes to training and testing machine learning models, blockchain can keep data secure and promote data exchange. In addition, it paves the way for the creation of timely prediction models using several data sources by leveraging distributed computing resources (like IoT). This is crucial for deep learning processes, which need a lot of processing time. However, distributed systems are more difficult to monitor and regulate than centralized ones, and blockchain systems will create a massive quantity of data from a variety of sources. The best blockchain mechanism designs need accurate data analysis and predictions of system behaviors. Data verification, as well as the detection of harmful assaults and dishonest transactions on the blockchain, may be aided by machine learning. There is a lot to gain from studying how to merge the two technologies from different perspectives.