Multi-Party Computation (MPC) is a cryptographic protocol enabling multiple parties to jointly compute a function over their private inputs without disclosing those inputs to each other. This technology ensures data privacy while allowing collaborative data processing or decision-making. MPC allows for secure operations where individual data remains confidential, even from the other participants in the computation. It forms a fundamental building block for advanced privacy-preserving applications in digital systems.
Context
In the cryptocurrency and blockchain domain, Multi-Party Computation is gaining traction for enhancing security and privacy in various applications. It is used for secure key management, threshold signatures and confidential transactions, reducing reliance on single points of failure. News often highlights MPC’s role in improving the security of digital asset custodianship and decentralized exchanges. This technology is critical for advancing the utility and adoption of privacy-centric blockchain solutions.
This research introduces a novel batch-processing mechanism for Automated Market Makers, fundamentally mitigating Miner Extractable Value and fostering equitable transaction execution.
This research pioneers novel zero-knowledge proof protocols, including HammR and CROSS, leveraging coding theory to secure digital signatures against emerging quantum threats.
MultiBank Group's strategic investment in Mavryk Network establishes a compliant, scalable framework for tokenizing $10 billion in UAE real estate, enhancing market accessibility and operational efficiency for institutional asset management.
This research pioneers a robust, highly efficient threshold ECDSA protocol, dramatically reducing communication and verification costs for securing decentralized systems.
This research introduces a novel lattice-based non-interactive distributed key generation protocol, enabling quantum-resistant, secure key management for future decentralized systems.
This integration establishes a secure, enterprise-grade framework for managing digital asset treasuries, enhancing operational resilience and strategic value realization for the biopharmaceutical firm.
Threshold cryptography fundamentally redefines digital asset security by distributing key fragments, enabling seamless user experiences and eliminating single points of failure.
New quantum-safe threshold ML-DSA signatures, using MPC, enable secure, collaborative signing for public blockchains, protecting against future quantum threats.
This research introduces a blockchain-secured framework for multi-party federated learning, enabling privacy-preserving collaboration and verifiable feature sharing through a novel consensus mechanism, significantly enhancing efficiency.
SecureVFL integrates a novel Proof of Feature Sharing consensus with replicated secret sharing on a permissioned blockchain, enabling robust, private, and efficient multi-party federated learning.
Silentflow pioneers TEE-assisted MPC, eliminating communication bottlenecks in Correlated Oblivious Transfer for real-time edge inference, advancing privacy-preserving computation.
This research introduces novel code-based zero-knowledge proofs, including HammR and a syndrome decoding protocol, fundamentally advancing quantum-resilient cryptography and secure digital signatures.
SecureVFL integrates a permissioned blockchain, a novel Proof of Feature Sharing consensus, and Replicated Secret Sharing for private, verifiable multi-party federated learning.
Nil Message Compute introduces a cryptographic framework for secure, private, and scalable decentralized computation, transcending traditional blockchain limitations.
A novel cryptographic primitive distributes signing authority across multiple parties, fundamentally mitigating single points of failure and bolstering decentralized system resilience.
This paper illuminates how cryptographic commitment schemes are foundational for achieving robust security and privacy in diverse multi-party computation applications.
A new Universally Composable Threshold FHE decryption protocol achieves high throughput via an offline-online structure, enabling real-time confidential computation.
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