mirror of
https://github.com/privacy-scaling-explorations/pse.dev.git
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37 lines
2.1 KiB
TypeScript
37 lines
2.1 KiB
TypeScript
import { ProjectCategory, ProjectInterface, ProjectStatus } from "@/lib/types"
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export const mpcStats: ProjectInterface = {
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id: "mpc-stats",
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image: "mpc-stats.png",
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name: "MPCStats",
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category: ProjectCategory.APPLICATION,
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projectStatus: ProjectStatus.ACTIVE,
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section: "pse",
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content: {
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en: {
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tldr: "A framework for private and verifiable statistical analysis across multiple data providers.",
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description: `
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## Overview
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MPCStats is a framework that enables data consumers to query statistical computations across multiple data providers while ensuring privacy and result correctness. By integrating privacy-preserving technologies such as ZKP, MPC, and FHE, our goal is to provide tools and guidance for integrating privacy-preserving analysis into their workflows. We also aim to identify real-world applications that can benefit from this framework.
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## Features
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- **Privacy-preserving and verifiable statistical analysis**: Allows data providers to keep their inputs confidential while giving data consumers the assurance that computations are performed accurately and securely.
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- **Data validity**: Integrates TLSNotary to authenticate inputs from verified web sources, ensuring data consumers can trust that data inputs are genuine and accurate.
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## Use Cases
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- **Cross-department data sharing and surveys**: Enables secure, private data sharing across government departments for streamlined operations and collaborative analysis.
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- **Healthcare research**: Aggregates data from sources such as fitness apps and sleep trackers, allowing researchers to uncover relationships between health factors, such as fitness and sleep patterns.
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- **Salary survey**: A verifiable and anonymous alternative to platforms like Glassdoor, where users can contribute salary data with privacy guarantees.
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`,
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},
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},
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links: {
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github: "https://github.com/ZKStats",
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website: "https://t.me/mpcstats",
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},
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tags: {
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keywords: ["MPC", "statistics", "data analysis"],
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themes: ["build"],
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types: ["Legos/dev tools", "Lego sets/toolkits"],
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builtWith: ["MP-SPDZ", "tlsn", "python"],
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},
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}
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