Enhancing MLOps with Blockchain: Decentralized Security for AI Pipelines
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P110Keywords:
MLOps, Blockchain, Security, AI Pipelines, Smart Contracts, Transparency, Decentralization, Model IntegrityAbstract
The rapid advancement of Machine Learning (ML) and Artificial Intelligence (AI) technologies has led to the increasing adoption of MLOps (Machine Learning Operations) frameworks to automate and streamline the development, deployment, and monitoring of AI models. However, the widespread integration of AI systems has raised significant concerns regarding the security, privacy, and transparency of AI pipelines. Traditional centralized security models are often vulnerable to data breaches, model manipulation, and other adversarial attacks. To address these challenges, blockchain technology offers a decentralized, immutable, and transparent approach that can enhance the security and integrity of MLOps pipelines. Blockchain enables secure data storage, verifiable data provenance, and tamper-proof record-keeping, which are critical for maintaining the trustworthiness of AI models. Furthermore, the integration of smart contracts in blockchain-based MLOps systems facilitates automation and ensures compliance with regulatory requirements. This paper explores how blockchain can be leveraged to fortify MLOps frameworks by providing a decentralized security layer that enhances transparency, reduces trust issues, and ensures the integrity of both data and models throughout the AI lifecycle
References
[1] R. K. Gupta and N. M. Jha, "Blockchain technology for securing AI models," Journal of Computer Security, vol. 28, no. 4, pp. 405-426, 2021.
[2] S. Y. Lee, H. S. Kim, and J. K. Lee, "Securing machine learning models with blockchain: Challenges and solutions," IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5678-5686, 2021.
[3] S. Zohar and A. L. Widgery, "Decentralized blockchain-based AI pipelines: A new paradigm for model security," Computing Research Repository, vol. 12, pp. 36-49, 2020.
[4] N. Ghani, "Blockchain-based smart contracts for AI model verification and validation," Journal of Artificial Intelligence Research, vol. 46, pp. 145-162, 2020.
[5] P. T. Koutsou and J. P. D. H. Cloutier, "Decentralized data storage systems for AI models: A blockchain approach," Journal of Cloud Computing: Advances, Systems, and Applications, vol. 9, no. 3, pp. 238-250, 2021.
[6] J. F. Watson, "Blockchain and machine learning: A complementary relationship," IEEE Internet of Things Journal, vol. 8, no. 1, pp. 101-110, 2020.
[7] M. A. Alsadi and M. A. Rahman, "Enhancing the security of machine learning with blockchain technologies," Journal of Computing and Security, vol. 45, no. 2, pp. 55-64, 2020.
[8] T. Lee and B. K. Tso, "A review of decentralized machine learning models for blockchain-based MLOps," International Journal of Artificial Intelligence and Applications, vol. 9, no. 6, pp. 97-108, 2021.
[9] M. M. Montano and J. C. Carroll, "Privacy-preserving machine learning using blockchain for secure data storage and sharing," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2951-2963, 2020.
[10] P. R. Chattopadhyay and S. D. Gupta, "Blockchain and AI security in industrial IoT systems: A review," IEEE Access, vol. 8, pp. 12241-12256, 2021.
[11] D. A. Bell and E. D. Scott, "Blockchain in AI: The role of transparency and immutability in machine learning security," Journal of AI Security, vol. 3, no. 2, pp. 25-39, 2021.
[12] H. L. Lee, "Smart contracts and automation in AI-driven systems," Journal of Blockchain Technology, vol. 5, no. 1, pp. 75-88, 2020.
[13] R. A. Sabater and E. M. Jackson, "Federated learning and blockchain: A new frontier for secure AI," IEEE Transactions on AI Security, vol. 7, no. 3, pp. 51-65, 2021.
[14] L. S. Miller, "Zero-knowledge proofs in blockchain-based AI systems," International Journal of Cryptography and Blockchain, vol. 14, no. 2, pp. 109-121, 2020.
[15] S. H. Zhang, "Energy consumption in blockchain-based systems for machine learning," IEEE Transactions on Blockchain Technology, vol. 10, no. 3, pp. 45-59, 2020.
[16] T. W. Patel and A. G. Fisher, "Cost implications of blockchain in MLOps: A financial analysis," Journal of Computational Economics, vol. 13, no. 4, pp. 123-137, 2021.
[17] T. Patel and B. D. Morrison, "Decentralized finance and blockchain: Opportunities for AI-driven financial systems," Journal of Financial Technology, vol. 5, no. 1, pp. 45-59, 2021.
[18] S. D. Campbell and M. E. Taylor, "Blockchain and machine learning: Building interdisciplinary research agendas," International Journal of Research in Artificial Intelligence, vol. 7, no. 2, pp. 101-113, 2021.
[19] Lakshmi Narasimha Raju Mudunuri, “AI Powered Supplier Selection: Finding the Perfect Fit in Supply Chain Management”, IJIASE, January-December 2021, Vol 7; 211-231.
