Bitcoin Integrates Cryptographic Accumulators, Enabling Private, Censorship-Resistant Transactions
This breakthrough proposes Bitcoin's architectural shift to cryptographic accumulators, enabling untraceable transaction blobs for enhanced privacy and fungibility.
Quantum-Resistant Framework Secures Cryptocurrency Transactions with Advanced Cryptography and Consensus
This research introduces a quantum-resistant blockchain framework, integrating lattice-based cryptography and an optimized consensus mechanism to safeguard future digital finance.
Quantifying MEV-Share Privacy with Aggregate Hints
Introduces Differentially-Private aggregate hints, enabling users to formally quantify privacy loss in MEV-Share for equitable extraction.
Quantum Crypto Guard: Post-Quantum Secure, Scalable, Private Blockchain Framework
Introducing Quantum Crypto Guard (QCG-ST), a novel blockchain framework integrating lattice-based cryptography and a sharded Proof-of-Stake consensus for quantum-resistant, scalable, and private transactions.
Merklized Transactions Enhance Blockchain Data Privacy and Granular Scalability
A novel Merkle-based transaction structure enables granular data redaction and lightweight verification, enhancing blockchain privacy and compliance.
Merklized Transactions Enable Granular Data Privacy and Scalable Verification
Merklized transactions redefine blockchain data handling, allowing granular verification and redaction for enhanced privacy and compliance without altering core immutability.
Zero-Knowledge Proofs: Bridging Theory to Practical Blockchain Privacy and Scale
Zero-knowledge proofs enable verifiable computation without revealing underlying data, fundamentally transforming blockchain privacy, security, and scalability for decentralized systems.
Regulatable Privacy-Preserving Smart Contracts Balance Confidentiality and Oversight
A novel framework enables selective data disclosure and regulatory traceability in account-based smart contracts, advancing privacy for decentralized applications.
Quantifying Transaction Privacy in MEV-Share with Differential Hints
A new mechanism introduces differentially private aggregate hints, allowing users to quantify privacy loss and optimize rebates in MEV extraction.
