Cryptanalysis Exposes Algebraic VDF Security Flaw Requiring New Consensus Primitives
Cryptographers demonstrated that parallel computing defeats the sequential delay assumption in algebraic VDFs, invalidating a core primitive for secure on-chain randomness.
Modular Framework Composes Verifiable Proofs, Scaling Sequential Computation Integrity
A new Verifiable Evaluation Scheme enables composable proof pipelines, drastically reducing overhead for complex, sequential computations like ZK-ML.
HyperLog Vector Commitment Enables Logarithmic Proofs for Universal Composability
HyperLog introduces an Integrated Homomorphic Commitment primitive, achieving $O(log N)$ proof size for state verification, fundamentally enhancing L2 scalability and security.
Silently Verifiable Proofs Enable Constant-Cost Batch Verification for Private Analytics
Silently Verifiable Proofs introduce a cryptographic primitive allowing servers to verify infinite proof batches by exchanging a single 128-bit string, fundamentally solving private analytics scalability.
FRI Protocol Enables Poly-Logarithmic Data Availability Sampling without Trusted Setup
FRIDA, a new primitive, leverages the FRI proximity test to construct a vector commitment, enabling non-trusted-setup DAS with $O(log^2 N)$ communication overhead.
Optimal Prover Time Unlocks Succinct Zero-Knowledge Proof Scalability
This breakthrough ZKP system achieves optimal linear prover time alongside succinct verification, resolving the fundamental trade-off between computational cost and proof size.
Expander Signatures Decouple Signature Generation Cost from Verification Complexity
This novel cryptographic primitive allows a powerful signer to generate all future signatures simultaneously, enabling constant-size verification on resource-limited devices.
Multivariate Signatures Secure Post-Quantum Multi-Party Blockchain Transactions
MV-MSS introduces a post-quantum, identity-based multi-signature scheme, leveraging the MQ problem to deliver compact, efficient on-chain authentication.
Zero-Knowledge Proof of Training Secures Decentralized AI Consensus and Privacy
ZKPoT uses zk-SNARKs to cryptographically validate decentralized machine learning contributions without revealing sensitive data, solving the privacy-efficiency-decentralization trilemma for federated systems.
