Zero-knowledge machine learning application in blockchain for decentralized computing
This dissertation introduces a novel decentralized application, ZKaggle, designed to facilitate a collaborative yet secure platform for computational task sharing and verification, capitalizing on blockchain technology and Zero-Knowledge Proofs (ZKPs). The development leverages the Filecoin Hyper...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2023
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172608 |
| _version_ | 1811696343660363776 |
|---|---|
| author | Li, Yihan |
| author2 | Mohammed Yakoob Siyal |
| author_facet | Mohammed Yakoob Siyal Li, Yihan |
| author_sort | Li, Yihan |
| collection | NTU |
| description | This dissertation introduces a novel decentralized application, ZKaggle, designed to
facilitate a collaborative yet secure platform for computational task sharing and
verification, capitalizing on blockchain technology and Zero-Knowledge Proofs
(ZKPs). The development leverages the Filecoin Hyperspace Testnet for deploying
smart contracts and Vercel for front-end deployment, employing Next.js to ensure a
responsive user interface. The application enables users to act as bounty providers or
hunters, engaging in verifiable and monetizable computational tasks.
A seamless workflow encompassing task creation, execution, submission, and
verification is delineated, underlining the transparent and user-centric design of the
platform. Compared to other projects aiming to decentralize computation, our work
expands their use case and incorporates decentralized storage to enhance user
experience.
After multiple experiments, we have successful deployment and functionality with
simpler machine learning models, such as handwritten digit recognition. However,
the scalability concerning more complex models poses a significant challenge due to
blockchain's performance constraints.
To address this, a myriad of future recommendations is proposed, focusing on
scaling to accommodate intricate models, on-chain verification optimization, user
interface enhancement, cross-platform compatibility, security fortification, and
community building.
Through a blend of modern technologies, frameworks, and cryptographic protocols,
the dissertation lays the groundwork for a robust, user-friendly platform, paving the
way for further innovation in decentralized computing and machine learning
communities. |
| first_indexed | 2024-10-01T07:37:52Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/172608 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:37:52Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1726082023-12-15T15:45:52Z Zero-knowledge machine learning application in blockchain for decentralized computing Li, Yihan Mohammed Yakoob Siyal School of Electrical and Electronic Engineering EYAKOOB@ntu.edu.sg Engineering::Computer science and engineering::Software::Software engineering This dissertation introduces a novel decentralized application, ZKaggle, designed to facilitate a collaborative yet secure platform for computational task sharing and verification, capitalizing on blockchain technology and Zero-Knowledge Proofs (ZKPs). The development leverages the Filecoin Hyperspace Testnet for deploying smart contracts and Vercel for front-end deployment, employing Next.js to ensure a responsive user interface. The application enables users to act as bounty providers or hunters, engaging in verifiable and monetizable computational tasks. A seamless workflow encompassing task creation, execution, submission, and verification is delineated, underlining the transparent and user-centric design of the platform. Compared to other projects aiming to decentralize computation, our work expands their use case and incorporates decentralized storage to enhance user experience. After multiple experiments, we have successful deployment and functionality with simpler machine learning models, such as handwritten digit recognition. However, the scalability concerning more complex models poses a significant challenge due to blockchain's performance constraints. To address this, a myriad of future recommendations is proposed, focusing on scaling to accommodate intricate models, on-chain verification optimization, user interface enhancement, cross-platform compatibility, security fortification, and community building. Through a blend of modern technologies, frameworks, and cryptographic protocols, the dissertation lays the groundwork for a robust, user-friendly platform, paving the way for further innovation in decentralized computing and machine learning communities. Master of Science (Signal Processing) 2023-12-15T12:49:54Z 2023-12-15T12:49:54Z 2023 Thesis-Master by Coursework Li, Y. (2023). Zero-knowledge machine learning application in blockchain for decentralized computing. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/172608 https://hdl.handle.net/10356/172608 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Software::Software engineering Li, Yihan Zero-knowledge machine learning application in blockchain for decentralized computing |
| title | Zero-knowledge machine learning application in blockchain for decentralized computing |
| title_full | Zero-knowledge machine learning application in blockchain for decentralized computing |
| title_fullStr | Zero-knowledge machine learning application in blockchain for decentralized computing |
| title_full_unstemmed | Zero-knowledge machine learning application in blockchain for decentralized computing |
| title_short | Zero-knowledge machine learning application in blockchain for decentralized computing |
| title_sort | zero knowledge machine learning application in blockchain for decentralized computing |
| topic | Engineering::Computer science and engineering::Software::Software engineering |
| url | https://hdl.handle.net/10356/172608 |
| work_keys_str_mv | AT liyihan zeroknowledgemachinelearningapplicationinblockchainfordecentralizedcomputing |