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  • Nesa Docs
    • Introduction to Nesa
    • Overview of the Nesa System
      • AI Models: Repository, Standardization, Uniformity
      • Users: Why Do We Need Private Inference?
      • Node Runners: Doing Inference and Earning $NES
    • Organization of the Documentation
  • Technical Designs
    • Decentralized Inference
      • Overview
      • Model Partitioning and Deep Network Sharding
      • Dynamic Sharding of Arbitrary Neural Networks
      • Cache Optimization to Enhance Efficiency
      • BSNS with Parameter-efficient Fine-tuning via Adapters
      • Enhanced MTPP Slicing of Topological Order
      • Swarm Topology
      • Additional: Free-Riding Prevention
    • Security and Privacy
      • Overview
      • Hardware Side: Trusted Execution Environments (TEEs)
      • Software/algorithm Side: Model Verification
        • Zero-knowledge Machine Learning (ZKML)
        • Consensus-based Distribution Verification (CDV)
      • Software/algorithm Side: Data Encryption
        • Visioning: Homomorphic Encryption
        • Implementation: Split Learning (HE)
      • Additional Info
        • Additional Info: Trusted Execution Environments (TEEs)
        • Additional Info: Software-based Approaches
    • Overview of $NES
      • $NES Utility
    • The First Application on Nesa: DNA X
    • Definitions
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      • Dynamic Model Versioning and Fork Management
      • Nesa's Utility Suite
      • The AI Kernel Market
      • Privacy Technology
        • Trusted Execution Environment (TEE)
        • Secure Multi-Party Computation (MPC)
        • Verifiable Random Function (VRF)
        • Zero-Knowledge Proof (ZKP)
      • The Integration of Evolutionary AI to Evolve the Nesa Ecosystem
      • Interoperability and Nesa Future Plans
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  1. Technical Designs
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  3. Privacy Technology

Secure Multi-Party Computation (MPC)

Nesa employs Secure Multiparty Computation (SMPC or MPC) for provable cryptographic security across the network. SMPC is a cryptographic protocol that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. Each participant in the computation has a piece of the overall data puzzle, yet none can see the other parties’ pieces. This method ensures that intermediate information remains undisclosed to any participating party throughout the process, with only the final output being revealed to the designated recipient.

On Nesa, this is crucial for tasks where both data privacy and collaboration are necessary. However, due to the intensive computational requirements generally associated with SMPC, we have optimized its application to be restricted to lightweight tasks throughout the network. This selective application allows us to benefit from the strong provable cryptographic security guarantees of SMPC where it matters most, without overwhelming the system with undue computational processing demands.

As a result, our project not only adheres to rigorous security standards but also maintains a high level of practicality and performance efficiency when handling the strictest data confidentiality measures.

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Last updated 1 year ago