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Deepti Sharda

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#federated learning Open access Oct 2026

BlockSecML: A Privacy-Preserving Machine Learning Framework Using Homomorphic Encryption, Advanced Encryption Standard-256 (AES-256), and Blockchain

BlockSecML is presented, a framework that brings together three technologies - Advanced Encryption Standard (AES)-256-Galois/Counter Mode encryption, Paillier partially homomorphic encryption (PHE), and Hyperledger Fabric blockchain - into a single working pipeline for privacy-preserving federated machine learning.

Deepti Sharda, Deeksha Gupta, Navdeep Kaur et al. · 0 citations

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