pse.dev/content/projects/mpc-stats.md

id, name, image, section, projectStatus, category, tldr, tags, links

id	name	image	section	projectStatus	category	tldr	tags	links
mpc-stats	MPCStats	mpc-stats.png	pse	inactive	applications	A framework for private and verifiable statistical analysis across multiple data providers.	keywords themes types builtWith MPC statistics data analysis build Legos/dev tools Lego sets/toolkits MP-SPDZ tlsn python	github website https://github.com/MPCStats https://mpcstats.github.io/docs

Overview

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.

Features

• 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.
• Data validity: Integrates TLSNotary to authenticate inputs from verified web sources, ensuring data consumers can trust that data inputs are genuine and accurate.

Use Cases

• Cross-department data sharing and surveys: Enables secure, private data sharing across government departments for streamlined operations and collaborative analysis.
• 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.
• Salary survey: A verifiable and anonymous alternative to platforms like Glassdoor, where users can contribute salary data with privacy guarantees.