mirror of
https://github.com/privacy-scaling-explorations/pse.dev.git
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34 lines
1.5 KiB
TypeScript
34 lines
1.5 KiB
TypeScript
import {
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ProjectCategory,
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ProjectContent,
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ProjectInterface,
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ProjectStatus,
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} from '@/lib/types'
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const content: ProjectContent = {
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en: {
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tldr: 'ZKML (Zero-Knowledge Machine Learning) leverages zero-knowledge proofs for privacy-preserving machine learning, enabling model and data privacy with transparent verification.',
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description:
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'ZKML is a solution that combines the power of zero-knowledge proofs (ZKPs) and machine learning to address the privacy concerns in traditional machine learning. It provides a platform for machine learning developers to convert their TensorFlow Keras models into ZK-compatible versions, ensuring model privacy, data privacy, and transparent verification. ZKML can be used to verify if a specific machine learning model was used to generate a particular piece of content, without revealing the input or the model used. It has potential use cases in on-chain biometric authentication, private data marketplace, proprietary ML model sharing, and AIGC NFTs.',
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},
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}
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export const zkml: ProjectInterface = {
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id: 'zkml',
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projectStatus: ProjectStatus.INACTIVE,
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category: ProjectCategory.RESEARCH,
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section: 'pse',
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image: 'zkml.png',
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name: 'ZKML',
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content,
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links: {
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github: 'https://github.com/socathie/circomlib-ml',
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},
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tags: {
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keywords: ['Anonymity/privacy', 'Scaling'],
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themes: ['research'],
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types: ['Proof of concept', 'Infrastructure/protocol'],
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builtWith: ['circom', 'halo2', 'nova'],
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},
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}
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