Ciphertext operations involve performing computations directly on encrypted data without any prior decryption. This cryptographic method permits processing sensitive information while it remains confidential, preventing exposure of the underlying plaintext. Such operations are fundamental to preserving privacy in various digital transactions and data analysis tasks. They represent a significant advancement in data security.
Context
Technologies like homomorphic encryption enable secure computation on private blockchain data or off-chain information used by decentralized applications. This advancement addresses critical privacy concerns, allowing for verifiable computations on confidential financial records without revealing the data itself. The application of these operations is growing in privacy-focused protocols.
A novel FHE scheme optimizes encrypted matrix arithmetic, delivering an 80x speedup crucial for practical, privacy-preserving on-chain AI and data analysis.
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