Protecting Predictive Analytics With Multi-Factor Authentication, Distributed Ledger Technology, and Federated Learning for Improved Data Privacy

Abstract

The research proposes a privacy-preserving predictive analytics paradigm that combines Federated Learning (FL), Distributed Ledger Technology (DLT), and Multi-Factor Authentication (MFA) to strengthen data privacy and security. FL supports decentralized model training, avoiding data centralization risks, and DLT protects the integrity of data through immutable and transparent blockchains. MFA strengthens access control through multi-layered authentication, reducing unauthorized access risks. The suggested model performs better than existing solutions such as Trusted Execution Environments (TEEs) and Attribute-Based Encryption (ABE) with accuracy of 93%, efficiency of 92%, and recall of 94%. The hybrid model ensures secure, scalable, and privacy-focused predictive analytics and can be applied in healthcare, finance, and IoT-based environments. This framework provides a strong solution to improve trust, transparency, and operational effectiveness in predictive modelling in sensitive domains by resolving confidentiality issues without compromising high predictive accuracy. Optimization methods and post-quantum cryptography will be investigated in future work for further security.