mirror of
https://github.com/privacy-scaling-explorations/pse.dev.git
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33 lines
1.4 KiB
TypeScript
33 lines
1.4 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: `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.ACTIVE,
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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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