Zero-Knowledge Machine Learning Survey Categorizes Foundational Concepts and Challenges
This paper provides the first comprehensive categorization of Zero-Knowledge Machine Learning (ZKML), offering a critical framework to advance privacy-preserving AI and model integrity.
Zero-Knowledge Proofs Secure Large Language Models with Verifiable Privacy
Zero-Knowledge Proofs enable Large Language Models to operate with provable privacy and integrity, fostering trust in AI systems without exposing sensitive data.
Adaptive Consensus Optimizes Distributed Systems for Dynamic Data Workloads
A novel consensus protocol dynamically adjusts data replication and quorum size using erasure coding, enhancing availability and performance in volatile cloud environments.
Anonymous Verifiable Credentials Eliminate Tracking While Preserving Digital Identity Verification
Anonymous Verifiable Credentials combine unlinkable authentication and verifiable credentials, using service-specific pseudonyms for private identity.
V-ZOR: Quantum-Driven ZKP Oracle Relays for Verifiable Cross-Chain Communication
A novel verifiable oracle relay, V-ZOR, integrates zero-knowledge proofs and restaking to secure cross-chain data, mitigating over $2.8 billion in risks.
Optimizing Verifiable Delay Function Verification for Ethereum Smart Contracts
This research significantly reduces the gas cost and proof size for Pietrzak's Verifiable Delay Function on Ethereum, enhancing practical blockchain integration.
Zero-Knowledge Commitment Enables Private, Verifiable Mechanism Execution without Mediators
A novel framework leverages zero-knowledge proofs to allow mechanism designers to commit to hidden rules, proving incentive properties and outcome correctness without disclosing the mechanism itself, thereby eliminating trusted intermediaries.
Orion: High-Throughput Asynchronous BFT with VDF Leader Election
A novel asynchronous Byzantine Fault Tolerant protocol, Orion, uses verifiable delay functions for leader election and pipelined processing to achieve optimal resilience and high throughput.
Formalizing Maximal Extractable Value for Provable Blockchain Security
This research establishes a rigorous, abstract model of MEV to enable formal security proofs against economic attacks in decentralized systems.
