Homomorphic secret sharing is a cryptographic method combining secret sharing with homomorphic encryption properties. This technique permits computations to occur directly on fragmented, encrypted data without first reassembling or decrypting the original information. It ensures that individual data shares remain confidential while enabling collaborative data processing. This approach significantly enhances privacy and security for distributed computational tasks.
Context
Homomorphic secret sharing represents an advanced cryptographic concept with significant implications for privacy-preserving computations, occasionally appearing in highly technical crypto news. Its application holds promise for secure multi-party computation and verifiable off-chain processing in blockchain systems. Further research and development are concentrating on improving its efficiency and practical deployment within decentralized environments.
A novel succinct oblivious tensor evaluation primitive, secured by Learning With Errors, enables adaptively-secure laconic function evaluation and optimal trapdoor hashing, advancing private verifiable computation.
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