Self-Stabilizing Replicated State Machines Resist Byzantine and Recurring Transient Faults
This paper introduces the first protocol for repeated Byzantine agreement that integrates self-stabilization, enabling distributed systems to autonomously recover from both malicious and transient errors.
Zero-Knowledge Proofs Enhance Digital Identity Data Minimisation
This research demonstrates how zero-knowledge proofs can resolve the inherent tension between digital identity verifiability and data minimisation, enabling privacy-preserving attribute attestations.
Decentralized E-Voting Secures Democratic Processes with Privacy and Verifiability
This protocol fuses EUDI, Zero-Knowledge Proofs, and TrustChain to enable secure, private, and verifiable mobile e-voting, enhancing democratic participation.
Achieving Statistical Non-Malleable Zero-Knowledge in Four Rounds
A novel four-round zero-knowledge argument achieves statistical non-malleability, advancing cryptographic proof systems beyond computational security.
Verifiable Data Aggregation Secures Decentralized Oracle Networks
A novel framework integrates cryptographic proofs with oracle aggregation, ensuring off-chain data integrity for robust smart contract execution.
Resilience-Oriented Consensus Protocol Enhances Blockchain System Robustness
RBFT protocol introduces weighted validation and late-node tolerance, fundamentally improving blockchain resilience, scalability, and performance against disruptions.
Formalizing MEV for Provable Blockchain Economic Security against Attacks
This research establishes a formal MEV theory using an abstract blockchain model, enabling provable security against economic attacks and enhancing network stability.
zk-SNARKs: Succinct Proofs for Verifiable, Private Computation
zk-SNARKs enable proving computational integrity and data privacy without revealing underlying information, revolutionizing secure and scalable decentralized systems.
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.
