docs: add mpc-stats doc

data/projects.ts

@@ -18,6 +18,7 @@ import { maci } from "./projects/maci"
 import { maciPlatform } from "./projects/maci-platform"
 import { mopro } from "./projects/mopro"
 import { mpc } from "./projects/mpc"
+import { mpcStats } from "./projects/mpc-stats"
 import { nfctap } from "./projects/nfctap"
 import { OpenPassport } from "./projects/openpassport"
 import { p0tion } from "./projects/p0tion"
@@ -53,6 +54,7 @@ export const projects: ProjectInterface[] = [
   rln,
   zkitter,
   mopro,
+  mpcStats,
   maci,
   wax,
   discreetly,

data/projects/mpc-stats.ts

@@ -0,0 +1,36 @@
+import { ProjectInterface, ProjectStatus } from "@/lib/types"
+
+export const mpcStats: ProjectInterface = {
+  id: "mpc-stats",
+  image: "mpc-stats.png",
+  name: "MPCStats",
+  section: "pse",
+  projectStatus: ProjectStatus.ACTIVE,
+  content: {
+    en: {
+      tldr: "A framework for private and verifiable statistical analysis across multiple data providers.",
+      description: `
+## Overview
+We are developing 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.
+- **Reusable demo infrastructure**: Enables rapid testing and feedback by deploying demos across various scenarios, allowing us to iterate quickly and refine potential applications.
+## 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.
+        `,
+    },
+  },
+  links: {
+    github: "https://github.com/ZKStats",
+    website: "https://t.me/mpcstats",
+  },
+  tags: {
+    keywords: ["MPC", "statistics", "data analysis"],
+    themes: ["build"],
+    types: ["Legos/dev tools", "Lego sets/toolkits", "Infrastructure/protocol", "Plugin",],
+    builtWith: ["MP-SPDZ", "tlsn", "python"],
+  },
+}