Machine Learning And Blockchain: Collaboration, Future Directions, And Challenges.pdf

A blockchain is a shared, distributed public ledger that stores transaction data in a chain of sequential blocks. Each block contains information from the previous one, and the mathematical structure of the blockchain makes it nearly impossible to fake. The term "blockchain" has transformed from a cryptography terminology to a buzzword, and its applications are much wider than just cryptocurrency. Scenarios involving data validation, auditing, and sharing can all consider applying blockchains.

Machine learning is a general terminology that includes various methods, such as machine learning, deep learning, and reinforcement learning. These methods are the core technology for big data analysis. The blockchain is a natural tool for sharing and handling big data from various sources through the incorporation of smart contracts. Blockchain can preserve data security and encourage data sharing when training and testing machine learning models.

The integration of blockchain and machine learning technologies can collaborate efficiently and effectively. Machine learning can facilitate the data verification process and identifying malicious attacks and dishonest transactions in the blockchain. The interdisciplinary research on combining the two technologies is of great potential.

The reviewed papers are summarized in Table 1 below. Papers that apply machine learning and blockchain techniques separately to various areas are listed in Table 2 below. The review is by no means exhaustive, but sufficient for Sections 3, 4, and 5 that introduce how different machine learning methods can be incorporated into the blockchain system.

A blockchain, literally speaking, is just a chain of digital blocks. Each block contains a certain amount of data, and the chain connects these data to form a distributed database. A newly created block includes multiple transactions collected from nodes and broadcasts to every node on the network. It can be accepted and added to the blockchain by nodes that have the same consensus protocol. Each added block includes information of the previous block in the chain.

The strategies to reach agreement of the new block (consensus) vary in different types of blockchain. The mathematical structure of the blockchain implies two essential properties: (i) the data (in block) is immutable; (ii) the distributed network with consensus allows users to communicate directly with each other and download a copy of the current ledger.

Depending on who can access to the blockchain and who can validate the data, the blockchain can be classified into public chains, private chains, and consortium chains. The comparison of three different types of blockchains is shown in Table 3.

Attribute
Public
Private
Consortium
Who run/manage the chain
All miners
One organization/user
Selected users
Permission to Access
No
Yes
Yes
Security
Nearly impossible to fake
Could be tampered
Could be tampered
Efficiency
Low
High
High
Centralized
No
Yes
Partial
Example
Bitcoin, Ethereum
IBM HyperLedger
Quorum