| Autor: |
Vid Keršič, Sašo Karakatič, Muhamed Turkanović |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Journal of King Saud University: Computer and Information Sciences, Vol 36, Iss 9, Pp 102207- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1319-1578 |
| DOI: |
10.1016/j.jksuci.2024.102207 |
| Popis: |
Zero-knowledge proofs introduce a mechanism to prove that certain computations were performed without revealing any underlying information and are used commonly in blockchain-based decentralized apps (dapps). This cryptographic technique addresses trust issues prevalent in blockchain applications, and has now been adapted for machine learning (ML) services, known as Zero-Knowledge Machine Learning (ZKML). By leveraging the distributed nature of blockchains, this approach enhances the trustworthiness of ML deployments, and opens up new possibilities for privacy-preserving and robust ML applications within dapps. This paper provides a comprehensive overview of the ZKML process and its critical components for verifying ML services on-chain. Furthermore, this paper explores how blockchain technology and smart contracts can offer verifiable, trustless proof that a specific ML model has been used correctly to perform inference, all without relying on a single trusted entity. Additionally, the paper compares and reviews existing frameworks for implementing ZKML in dapps, serving as a reference point for researchers interested in this emerging field. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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