Ever wondered how we’re going to keep all that AI data safe in the future? The same tech behind cryptocurrencies might just be the thing we need to protect our AI systems. Let’s dive in and see why!
The Need for Enhanced Data Security in AI
AI systems rely on large datasets to train models and make predictions. From healthcare to online shopping. A data breach can cause a lot negative consequences. Securing these datasets is important, and while encryption and secure storage methods exist, most of the time there is lack of transparency and tamper-proof (in blockchain, “tamper-proof” means that once data is recorded, it cannot be changed, making it highly secure) assurances needed in AI applications. Blockchain technology offers a solution that works well with AI’s need for data.
How Blockchain Secures AI Data
Blockchain technology, in easy words, is a system that spreads information across many computers and makes it very hard to change. Once data is recorded on the blockchain, it can’t be changed without changing all the data that came after it. And this is what makes it strong. This tamper-proof feature makes blockchain a good way for securing sensitive data transactions in AI systems.
What are the most important benefits:
Data Integrity: Blockchain creates records of data that cannot be changed and it also keeps the accuracy of the data that AI systems use for decision-making.
Transparency: Everyone in the blockchain network can see the same information, making it hard for anyone to change data without others noticing.
Decentralization: Removing a central authority reduces the risk of data breaches, there is not 1 server where are the data is stored, it is spread across many computers. Adding an additional layer of security.
Ted Talk:
I added a TED talk that talks about use the case of AI and blockchain in a different way. This shows the broad use and strong capabilities when you connect AI and blockchain
Real-World Applications and Use Cases
1.Healthcare: One area where the integration of AI and blockchain has shown good real-life use case, is in healthcare. For example, in their research, Tatineni (2022) discusses how combining AI and blockchain ensures data integrity, security, and accessibility in healthcare data management. By using blockchain, healthcare providers can guarantee that patient data remains tamper-proof, while AI systems can use this verified data to provide accurate diagnoses and treatment recommendations. In smart city healthcare applications, Rajawat et al. (2022) explores how blockchain and AI can improve the security of data in smart city environments, keeping the people privacy’s.
2.Industry Cyber-Physical Systems: In machinery in manufacturing like robotics or smart grids, blockchain ensures data integrity and trust in AI-driven processes. Hossain et al. (2024) explore how blockchain enhances security and traceability in cyber-physical systems, providing a tamper-proof record of all data transactions and giving trust in AI-powered systems.
Future of Blockchain and AI Integration
I see a lot of potential for AI and blockchain in the future. It can expand beyond healthcare or e-commerce, the applications are boundless. Chowdhury (2024) talk about how blockchain provides a tamper-proof mechanism for AI-driven data, giving the possibility for industries to improve their business intelligence processes securely.
There is also more and more use of internet of things, for example smart thermostats, wearable fitness trackers, and connected cars. Raparthi and Gayam (2021) talk about how blockchain and AI can enhance privacy and data security in IoT applications, creating a decentralised, tamper-proof platform for storing big amounts of data generated by connected devices.
By integrating AI with blockchain, future systems will be able to ensure that their data—whether it be financial, personal, or industrial—remains secure, trustworthy, and tamper-proof. The decentralised nature of blockchain will continue to complement AI’s growing role in data analysis, providing businesses and individuals with the security assurances they need in the digital age.
Challenges to Blockchain Securing AI Data
While blockchain shows promise in securing AI data, several obstacles need to be addressed. There are two issues I want to discuss a bit more in depth:
Scalability Issues
One of the biggest concerns with blockchain technology is scalability. So if the work amounts grow, how well can the system handle this?
Traditional blockchains, like Bitcoin and Ethereum, are designed to handle relatively small volumes of transactions compared to the huge amounts of data AI systems generate. It is like trying to fit a big lake through a small pipe. As more and more data is added to the blockchain, the system can become slow and expensive to use. Imagine if every time you wanted to use an AI application, you had to wait several minutes for it to process!
Possible solution: To overcome this challenge, new blockchain solutions such as Layer 2 scaling techniques, sharding, or hybrid blockchain models are being developed.
A lot of these new system are still being tested and we must see if there are able to process the big amount of data that AI uses.
High Energy Consumption
Probably a lot of you heard already that it cost a lot of energy to mine blocks, especially like Proof of Work (PoW) consensus algorithms. Some blockchain use even more electricity than entire countries. If AI and blockchain are combined, it could consume enormous amounts of energy.
Possible solution: There is some cryptocurrencies who are changing from PoW to more energy-efficient models such as Proof of Stake (PoS), but they do not have the same level of security.
Conclusion
Thinking about AI and blockchain creates many powerful possibilities. It can provide securing data, in a world where this becomes harder and harder. AI systems will only get bigger and more complicated, blockchain could be the decentralised, tamper proof system to keep the data integrity and security. Whether this is in healthcare or maybe in something we did not even think of.
References
Tatineni, S. (2022). Integrating AI, blockchain, and cloud technologies for data management in healthcare. Journal of Computer Engineering and Technology. Retrieved from https://www.researchgate.net/publication/378669034
Rajawat, A. S., Bedi, P., Goyal, S. B., & Shaw, R. N. (2022). AI and blockchain for healthcare data security in smart cities. In AI and IoT for Smart City (pp. 123-138). Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-981-16-7498-3_12
Chowdhury, R. H. (2024). Blockchain and AI: Driving the future of data security and business intelligence. World Journal of Advanced Research and Reviews. Retrieved from https://wjarr.com/content/blockchain-and-ai-driving-future-data-security-and-business-intelligence
Raparthi, M., & Gayam, S. R. (2021). Privacy-preserving IoT data management with blockchain and AI: A scholarly examination of decentralized data ownership and access control mechanisms. Internet of Things: Current Technologies and Applications. Retrieved from https://thesciencebrigade.com/iotecj/article/view/131
Hossain, M. I., Steigner, D. T., & Hussain, M. I. (2024). Enhancing data integrity and traceability in industry cyber-physical systems (ICPS) through blockchain technology. arXiv preprint. Retrieved from https://arxiv.org/abs/2405.04837
