mpc-protocols

Name: mpc-protocols
Availability: InStock
Author: sparq-org

Implement secure multi-party computation protocols for SPARQL queries.

8|1|Updated Jun 7, 2026

Search Tags:#data privacy #sparql #mpc #zero-knowledge proofs #secure computation

Ready to run

npx skills add https://github.com/sparq-org/sparq --skill mpc-protocols

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mpc-protocols
Source: https://github.com/sparq-org/sparq/tree/main/.claude/skills/mpc-protocols
Command: npx skills add https://github.com/sparq-org/sparq --skill mpc-protocols

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenges of secure multi-party computation (MPC) for SPARQL queries, ensuring privacy and correctness in collaborative data processing.

Core Features & Use Cases

Confidentiality and Correctness: Ensures that no party learns another's inputs and that the output is the right answer.
Input Authentication: Validates data authenticity using attested sources.
Malicious Security: Provides guarantees against actively cheating parties.
Use Case: When integrating MPC with the existing ZK estate for SPARQL queries or when sizing the feasibility of multi-party RDF data processing.

Quick Start

Run the 'mpc-protocols' skill to review the secure MPC protocols for SPARQL queries.

Frequently Asked Questions about mpc-protocols

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform secure multi-party computation on SPARQL queries without exposing private RDF data?▼

Secure multi-party computation (MPC) for SPARQL queries protects private RDF data by ensuring input confidentiality and correctness, preventing any participant from learning another's inputs during collaborative processing.

What does malicious security guarantee for multi-party SPARQL data processing?▼

Malicious security in multi-party SPARQL data processing provides cryptographic guarantees against actively cheating parties, ensuring that malicious actors cannot manipulate the computation or compromise the integrity of the shared query results.

Can I integrate zero-knowledge proofs with SPARQL queries for collaborative data integration?▼

Yes, zero-knowledge proofs can be integrated with SPARQL queries to validate data authenticity using attested sources, enabling secure collaborative RDF data processing and multi-party computation.

How do I validate input authenticity in secure multi-party SPARQL computations?▼

Input authentication in multi-party SPARQL computations validates data authenticity using attested sources, ensuring all participating parties provide verified and legitimate inputs before cryptographic query processing begins.

Does this secure MPC approach work for large-scale collaborative RDF data processing?▼

This secure MPC approach applies to collaborative SPARQL data processing and integration, enabling feasibility sizing for multi-party RDF data processing while maintaining confidentiality, correctness, and malicious security guarantees.