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33 lines
1.8 KiB
Markdown
33 lines
1.8 KiB
Markdown
---
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id: "mpc-stats"
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name: "MPCStats"
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image: "mpc-stats.webp"
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section: "pse"
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projectStatus: "inactive"
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category: "applications"
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tldr: "A framework for private and verifiable statistical analysis across multiple data providers."
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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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links:
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github: "https://github.com/MPCStats"
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website: "https://mpcstats.github.io/docs"
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---
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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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