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Ensuring Data Protection Using Machine Learning Integrating with Blockchain Technology

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Innovations in Computer Science and Engineering (ICICSE 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 565))

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Abstract

In recent years, the emergence of blockchain technology (blockchain) has become a singular, most tumultuous, and trending technology. The decentralized info in blockchain underscores information security and confidentiality. Also, the agreement mechanism in it makes positive that information is secured and legit. Throughout this paper, we have got coated the analysis on combining blockchain and machine learning technologies and demonstrate that they are attending to collaborate with efficiency and effectiveness. Machine learning might even be a general language that comes with a spread of methods, machine learning, deep learning, and reinforcement learning. These ways square measure the core technology for big information analysis. As a distributed and append-only ledger system, a blockchain can be a natural tool for sharing and handling massive information from varied sources through the incorporation of good contracts. Additional expressly, blockchain will shield information security and promote information sharing. It also permits various countries to utilize distributed computing powers, for instance, IoT, for developing on-time prediction models with varied sources of knowledge. Blockchain systems can also generate large amounts of useful data from completely different sources, and the knowledge domain analysis on combining the two technologies is of nice potential and the combination of machine learning and blockchain will give extremely precise results.

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Correspondence to Princy Diwan .

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Diwan, P., Khandelwal, B., Dewangan, B.K. (2023). Ensuring Data Protection Using Machine Learning Integrating with Blockchain Technology. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, vol 565. Springer, Singapore. https://doi.org/10.1007/978-981-19-7455-7_27

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