Homomorphic encryption is a form of encryption that allows computations to be performed on encrypted data without decrypting it first. The results of these computations, when decrypted, match the results that would have been obtained if the computations were performed on the original unencrypted data. This enables secure processing of sensitive information.
Context
The relevance of homomorphic encryption in the cryptocurrency and blockchain space is growing, particularly concerning privacy-preserving transactions and secure data analysis on decentralized networks. Current discussions often center on the computational cost associated with these schemes and the development of more efficient algorithms. Future advancements are anticipated in enabling complex smart contract operations and data analytics on encrypted blockchain data.
New HE-IOP primitive solves the integrity problem for approximate homomorphic encryption, enabling verifiable, private, outsourced computation for AI models.
This new cryptographic framework efficiently integrates Verifiable Computation with approximate Homomorphic Encryption, enabling trustless, private AI computation at scale.
This breakthrough uses Homomorphic Encryption to perform biometric verification directly on encrypted data, enabling a provably private and secure decentralized identity layer.
This research empirically evaluates secret leader election mechanisms in Ethereum Proof-of-Stake, revealing vulnerabilities to coordinated attacks despite individual protections.
This research introduces verifiable one-time programs, foundational for a novel single-round secure computation model, unlocking practical quantum-assisted cryptography with minimal resources.
A new primitive using Fully Homomorphic Encryption allows public blockchains to facilitate private, institutional-grade DeFi lending, addressing a critical confidentiality gap.
This research introduces novel ZK arguments for the CL cryptosystem, enabling private, verifiable computations in unknown order groups for enhanced privacy.
A novel framework enables third-party computation and efficient set updates for private set intersection, expanding its utility in dynamic, privacy-preserving distributed systems.
This research optimizes threshold ECDSA by leveraging homomorphic encryption, enabling robust, efficient distributed signing with reduced communication overhead for decentralized applications.
The Zama Protocol introduces a novel cross-chain confidentiality layer, leveraging Fully Homomorphic Encryption to enable smart contracts to process encrypted data without decryption, fostering ubiquitous on-chain privacy.
This collaboration establishes a verifiable, privacy-preserving AI infrastructure, empowering enterprises to deploy intelligent agents with cryptographic trust and auditable integrity at scale.
This research leverages Fully Homomorphic Encryption to enable on-chain computation over encrypted data, fundamentally transforming blockchain transparency into pervasive confidentiality.
A breakthrough in Fully Homomorphic Encryption bootstrapping slashes computation latency to microseconds, making on-chain confidential smart contracts viable.
This research introduces a protocol for confidential smart contracts, leveraging Fully Homomorphic Encryption to process encrypted data on-chain, securing sensitive information in decentralized applications.
This research introduces a novel batch-processing mechanism for Automated Market Makers, fundamentally mitigating Miner Extractable Value and fostering equitable transaction execution.
A novel blockchain-based meta-operating system unifies AI development and deployment across edge devices, leveraging homomorphic encryption for privacy.
Secure Multi-Party Computation enables joint function computation on private data, fostering privacy and collaboration across decentralized systems and sensitive applications.
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