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  • Trusted Model Aggregation With Zero-Knowledge Proofs in Federated . . .
    This paper proposes a new global model aggregation method based on using zero-knowledge federated learning (ZKFL) The purpose is to secure horizontal or P2P federated machine learning systems with shorter aggregation times, higher model accuracy, and lower system costs We use a model parameter-sharing Chord overlay network among all client hosts The overlay guarantees a trusted sharing of
  • Revocable Anonymous Credentials from Attribute-Based Encryption
    We introduce a credential verification protocol leveraging on Ciphertext-Policy Attribute-Based Encryption The protocol supports anonymous proof of predicates and revocation through accumulators
  • ZEBRA: Anonymous Credentials with Practical On-chain Verification and . . .
    ZEBRA - Zero-knowledge (Anonymous), batched, revocable and auditable credentials ZEBRA supports - Auditability - Authorized auditors identify the owner of a maliciously behaving user Revocation - as credentials are often lost or stolen, and credentials of malicious users need to be revoked On-chain verification - With the primary goal of minimizing the verification cost using ZK-SNARKs
  • zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated . . .
    The aggregator generates the aggregated model update w and leverages zero-knowledge proofs (ZKPs) to generate a proof π, and the clients will verify the proof to ensure that the training model updates are correctly aggregated zkFL guarantees the integrity of the data computed by the aggregator, and enhances security and trust in the
  • Zero Knowledge Proof based Gradient Aggregation for . . . - HackerNoon
    zkFL leverages zero-knowledge proofs (ZKPs) to tackle the issue of a malicious aggregator during the training model aggregation process
  • GitHub - xlab-si emmy: Library for zero-knowledge proof based . . .
    Emmy is a library for building protocols applications based on zero-knowledge proofs, for example anonymous credentials Zero-knowledge proofs are client-server protocols (in crypto terms also prover-verifier, where the prover takes on the role of the client, and the verifier takes on the role of the server) where the client proves a knowledge of a secret without actually revealing the secret
  • zk-creds: Flexible Anonymous Credentials from zkSNARKs and Existing . . .
    We present and build zk-creds, a protocol that uses general-purpose zero-knowledge proofs to 1) remove the need for credential issuers to hold signing keys: credentials can be issued to a bulletin board instantiated as a transparency log, Byzantine system, or even a blockchain; 2) convert existing iden-tity documents into anonymous credentials
  • verifiable-credentials-and-zero-knowledge-proof-systems. md
    In order to build anonymous credential systems, ZKPs can be combined with Verifiable Credentials to enhance user privacy This is a proposal to develop library support for Verifiable Credentials and recommend ZKP formats for different use cases and credential attributes
  • CredVault: A Credential Management System based on Zero-Knowledge . . .
    As digital identities become increasingly valuable and vulnerable, the protection of personal credentials has become a critical concern This paper introduces a fresh perspective on credential management, focusing on enhancing privacy and security We propose a user-centric approach that revolves around the idea of creating a safe space for digital credentials within decentralized wallets By
  • -: Zero-Knowledge Proof-Based Gradient Aggregation for Federated . . .
    zkFL zkFL: Zero-Knowledge Proof-Based Gradient Aggregation for Federated Learning Abstract: Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator FL can be a scalable machine learning solution in Big Data scenarios





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