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💼 Applications: Threshold Signatures & Privacy

Discover real-world MPC use cases in blockchain

Compute on encrypted data without revealing it

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Real-World Applications

MPC is transitioning from academic research to production deployments. From private voting to collaborative AI, organizations are using MPC to unlock value from data that was previously too sensitive to share. The key insight: **privacy and utility are not opposites**—MPC gives you both.

Modern MPC libraries (MP-SPDZ, CrypTen, TF Encrypted) make it practical to build privacy-preserving applications. Performance has improved 1000x in the last decade—what took hours in 2010 now takes seconds.

Interactive: Application Explorer

Explore how MPC solves real-world privacy problems across different domains.

🗳️

Private Voting Systems

Problem
Electronic voting requires privacy (secret ballot) and verifiability (honest tallying)
MPC Solution
MPC tallies votes without revealing individual ballots. Each voter splits their vote into shares.
Key Benefits
✓Voter privacy guaranteed cryptographically
✓No trusted authority needed
✓Publicly verifiable results
✓Resistant to coercion
Real-World Projects
•Helios Voting (academic elections)
•Swiss Post e-voting research
•Estonia exploring MPC voting
Challenges
Voter authentication, coercion-resistance, large-scale deployment
Impact
High - could enable trustless digital democracy

🔐 MPC in Blockchain

Blockchain and MPC are natural partners—both enable decentralized trust. Here's how MPC enhances blockchain security:

🔑
Threshold Signatures
Split private keys across multiple parties. t-of-n parties needed to sign transactions—no single point of failure.
Used by: Fireblocks, Coinbase Custody, ZenGo, Binance
💸
Private Transactions
MPC enables confidential smart contracts on public blockchains—amounts and parties hidden, logic verified.
Examples: Tornado Cash (discontinued), Aztec Network, Secret Network
🌉
Cross-Chain Bridges
MPC committees manage bridge contracts without trusted operators. Distributed custody of locked assets.
Examples: THORChain, Ren Protocol, Keep Network
🎲
Verifiable Randomness
MPC generates unbiased random numbers for lotteries, gaming, and validator selection—no party can predict output.
Examples: Chainlink VRF, Threshold Network, drand

Performance & Scalability

Typical Performance (2024)

AES encryption (GMW)~50ms
SHA-256 hash (Yao)~100ms
ECDSA signature (threshold)~500ms
Neural network inference (ABY3)~2s
Neural network training (SPDZ)~10min

Scaling Approaches

•Hardware acceleration: GPUs for matrix operations, FPGAs for garbling
•Preprocessing: Generate Beaver triples offline, use online when needed
•Circuit optimization: Minimize multiplications, use boolean circuits for comparisons
•Network optimization: Batch communication, use faster protocols (LAN vs WAN)

MPC Frameworks & Libraries

MP-SPDZ
General-purpose MPC compiler
Python-like language, 70+ protocols, active research
CrypTen (PyTorch)
Privacy-preserving ML by Meta
Easy integration with PyTorch, production-ready
TF Encrypted
Private TensorFlow by Cape Privacy
TensorFlow API, Keras support, federated learning
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