Homomorphic computation is a cryptographic technique that permits computations to be performed on encrypted data without first decrypting it. The result of this computation remains encrypted and, when decrypted, matches the result of the same computation performed on the unencrypted data. This preserves data privacy during processing.
Context
Homomorphic computation holds substantial promise for enhancing privacy in decentralized applications, particularly for sensitive operations involving personal data or confidential financial transactions. While still computationally intensive, advancements in this field are regularly reported as crucial for enabling private data analysis on public blockchains. Its practical application could significantly alter how user data is handled within Web3 services, addressing critical privacy concerns.
A novel framework integrates DABE, HE, SMPC, and blockchain to secure IoT federated learning, enabling privacy-preserving AI and verifiable data exchange.
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