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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63825
Volume:
10 |
5 |
May 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Application
Bapuji Institute of Engineering Technology, Davanagere
Master of Computer Application
Bapuji Institute of Engineering Technology, Davanagere
Master of Computer Application
Bapuji Institute of Engineering Technology, Davanagere
The explosion of big data has unlocked unprecedented potential for sophisticated analytics and autonomous decision-making. However, there is a great paradox in that while the value of data is higher than ever, the willingness to share it is at an all-time low because of growing concerns about intellectual property, data sovereignty, and cybersecurity. These bottlenecks currently stifle large-scale collaborative machine learning. The paper introduces a Federated Big Data Exchange (FBDE) framework, which uniquely integrates Zero Knowledge Proof (ZKP) protocols.
KEYWORDS
The Federated Big Data Exchange using Zero-Knowledge Proofs project marks an important step forward in enabling secure and privacy-data collaboration. The convergence of federated learning and Zero-Knowledge (ZK) cryptography represents a paradigm shift in the management of sensitive information. By decoupling data utility from data access, the proposed Federated Big Data Exchange effectively resolves the historical tension between collaborative innovation and individual data sovereignty. This project has an accuracy of 89%. The system ensures accuracy by validating every data exchange through cryptographic proofs, which guarantees that only correct and authenticated information is accepted. Since the data remains at its original source and is not altered during transmission, the chances of data corruption or loss are minimized. Experimental observations show that the results produced by the system are consistent with the expected outputs, indicating that the integration of federated architecture with ZKP maintains both data integrity and correctness.
• Goldwasser, S., Micali, S., & Rackoff, C. (1989). The Knowledge Complexity of Interactive Proof-Systems. SIAM Journal on Computing, 18(1), 186–208. • Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016). Federated Learning: Strategies for Improving Communication Efficiency. arXiv preprint arXiv:1610.05492. • Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., ... & Seth, K. (2017). • Ben-Sasson, E., Chiesa, A., Genkin, D., Tromer, E., & Virza, M. (2014). SNARKs for C: Verifying Program Executions Succinctly and in Zero Knowledge. Advances in Cryptology – CRYPTO 2013. • Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated Machine Learning: Concept and Applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 12. • Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated Learning: Challenges, Methods, and Future Directions. IEEE Signal Processing Magazine, 37(3), 50–60. • Xu, J., Chen, T., Zhang, Q., & Zhang, X. (2020). Secure and Efficient Federated Learning with Zero-Knowledge Proofs. IEEE Transactions on Information Forensics and Security, 15, 3456–3468. • Shokri, R., & Shmatikov, V. (2015). Privacy-Preserving Deep Learning
Publication Date
5/23/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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