Secure and efficient data analysis: exploring the intersection of machine learning and blockchain

While machine learning systems have many benefits and numerous applications, they often require access to large amounts of personal data in order to function effectively. Large companies (such as Facebook, Google, and Microsoft, for example) use tracking tools like cookies and connected devices and apps to gather that data. For this reason, many users raised concerns about data privacy and security.

Blockchain is a tool that aims to solve this specific issue. For that reason, we could say that blockchain and machine learning are a match made in heaven.

What is machine learning?

Machine learning (ML) is a subfield of artificial intelligence that deals with the development of algorithms that enable computers to learn from data and make predictions or decisions without being explicitly programmed. In simple words, machine learning focuses on creating intelligent systems that can process and learn from data to perform tasks more effectively and autonomously. This is done by creating models that can automatically identify patterns and relationships in the data provided.

What is blockchain?

Blockchain is a decentralized, distributed digital ledger that is used to record data in a secure and transparent way. Essentially, it is a database that is maintained by a network of computers with no central authority or middlemen. The security of the blockchain is based on cryptography, which makes it virtually impossible to tamper with the data. Each block is linked to the previous one through a cryptographic hash function, which creates a unique digital signature for each block. This means that any change made to a block will be immediately apparent and will break the chain.

Benefits of ML

Benefits of machine learning are many:

Increase in accuracy and efficiency: Machine learning algorithms can analyze large amounts of data and identify patterns that would be difficult or impossible for humans to detect.

Automatization: Repetitive tasks can be automated, which frees time for people to focus on more complex and creative projects they want to do. 

Personalization: Every user can have their personal experience tailored, which makes them perceive the product with more familiarity.

Fraud detection: Machine learning algorithms can identify fraudulent activity and prevent it before it occurs, reducing the risk of financial losses.

Drawbacks of ML

Most of those come from the ones who programmed it. 

For example, if data used to train the model is biased or simply incorrect, the model will provide invalid predictions. Also, when data is highly centralized, only a limited number of experts can validate and potentially modify it. However, this centralized control raises concerns about potential biases within the data.

A big concern for machine learning is, as mentioned, data privacy. 

For a machine learning model to learn about its user, it has to get their data. As we already said at the beginning, this data is provided via accounts on devices provided by Google, Apple, or Microsoft, for example. Personal data is stored on such platforms, but the problem arises when users are unable to see the quantity of their data that is used and stored and, at the same time, aren’t able to turn off their data collection.

Benefits of blockchain

Essentially, blockchain is a database, so what are its advantages over traditional databases? 

Security: Blockchain uses cryptography to secure and protect data from unauthorized access and tampering. Once a block of data is added to the chain, it is extremely difficult to modify or delete its data.

Decentralization: We are talking about a decentralized system, meaning that it is not controlled by any entity. This can lead to increased data transparency, as there is no central authority that can manipulate the data or delete it.

Transparency: These transactions are publicly visible and evident, allowing anyone to see and verify the data.

Efficiency: Blockchain can streamline and automate many processes, reducing the need for intermediaries and improving productivity.

Lower costs: Costs are reduced by eliminating intermediaries since anyone can join and help with hosting data on-chain; therefore, there is no need to build expensive data centers.

Increased privacy: Users are allowed to maintain control over their data, which increases their confidentiality. 

Drawbacks of blockchain

Compared to traditional databases, blockchain has only a few flaws:

Limited adoption: While traditional databases have been building applications for decades, the oldest and simplest blockchain (Bitcoin) is only a “teenager”.  Then again, unlike Blockchain, Ethereum, the first turing-complete blockchain is not even 10 years old.

Complexity: Blockchain technology can be complex and difficult to understand, making it challenging to implement and maintain.

So, how do blockchain and ML intersect?

The simplest answer to this question is that blockchain resolves most of the drawbacks of ML:

Data integrity: Data stored on the blockchain is transparent, immutable, and safer from breaches in security than centralized storage methods.

Privacy: Users choose what data to store on blockchain, and they own it; every person chooses what level of privacy they want.

Efficiency: With most blockchains having smart contract capabilities, machine learning models can be stored right on the blockchain and interact with data quickly as lighting. For larger models, blockchain could use integration with networks like IPFS or Arweave.

Use cases of ML in the biggest markets

ML automates tasks and analyzes data: which means it handles large datasets, which is the reason why it’s an important tool used in many industries. In these few examples of such markets, we will describe how ML empowers some of the largest businesses.

Healthcare

Using blockchain to store healthcare data gives power to anyone over their data; they control who can see it, and they control what can be seen. With machine learning being used as a help in some fields of healthcare, like cancer detection, integrating it with blockchain would give anyone with an internet connection the permission to check if their health is at risk while having complete control of their data.

Supply chain

Using blockchain to keep track of the supply chain is already happening with projects like origintrail, vechain, AirDAO, and Chainlink. Using blockchain on the supply chain makes the data transparent, trustworthy, and sustainable. Adding machine learning on top of the data would give producers insight into their products' data and provide customers with transparent information about the products they buy.  In addition to that, adding it would also help with cracking down on unethical production such as child labor, exploitation of workers, violation of legal regulations, etc. 

Finances

The biggest use of blockchain is currently related to ML. There are many examples to confirm that, such as fraud detection  - exchanges to determine if the token is real and has naturally grown or is a pump and dump. Machine learning could also be used for predictions of assets, and with the model being right on the blockchain, its speed and reaction time would be matchless.

If it is a good idea, why hasn't it been done?

Many projects merging blockchain and machine learning are currently live and very successful; the main problem with the complete merger of machine learning and blockchain is the price of computing on the blockchain. Most of the blockchains update their state with smart contracts, which means that every update to on-chain data must be done through smart contracts. This makes it impractical to perform ML on the blockchain, so the best solution to this is having a processing layer on-chain. Some of the blockchains that do this are [akash] (https://akash.network/) and [rendertoken] (https://rendertoken.com/). Networks like these enable people to run their code cheaply with security off-chain and then submit important results to the chain.

Projects that use machine learning on blockchain:

NUMERAI

Numerai uses blockchain to host the biggest data science tournament in the world. People submit their machine learning models on the chain and stake for their model; depending on the performance of their model, they either earn NMR tokens or lose their NMR stake. Check the current contestants' payout in $, and the number of ML models Numerai owns here:https://numer.ai/tournament.

fetch.ai

Fetch.ai is building infrastructure to create open, autonomous services. They are the closest to running machine learning directly on-chain.

To sum up

In conclusion, the intersection of machine learning and blockchain presents a promising avenue for secure and efficient data analysis. As machine learning systems continue to advance and become more prevalent in various industries, the need for robust privacy and security measures becomes increasingly crucial. The concerns surrounding data privacy and security have been growing as large companies collect vast amounts of personal data, raising questions about the ethical use and protection of this information.

Hey, you! What do you think?

They say knowledge has power only if you pass it on - we hope our blog post gave you valuable insight.

If you want to share your opinion or need a consultation regarding your ML and blockchain projects and data privacy, feel free to contact us. 

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