vCNN: Verifiable Convolutional Neural Network Based on zk-SNARKs

Author:

Lee Seunghwa1ORCID,Ko Hankyung2ORCID,Kim Jihye1ORCID,Oh Hyunok2ORCID

Affiliation:

1. Kookmin University, Seoul, South Korea

2. Hanyang University, Seoul, South Korea

Funder

Institute for Information & Communications Technology Promotion

Blockchain privacy preserving techniques based on data encryption

Blockchain scalability solutions supporting high performance/capacity transactions

A Study on Cryptographic Primitives for SNARK

Development of Hardware-centric Trusted Computing Base and Standard Protocol for Distributed Secure Data Box

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Reference35 articles.

1. Pinocchio

2. On the size of pairing-based non-interactive arguments;Groth

3. Quadratic span programs and succinct NIZKs without PCPs;Gennaro

4. Delegating computation: Interactive proofs for muggles;Goldwasser

5. Doubly-Efficient zkSNARKs Without Trusted Setup

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