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✅ Master Multi-Party Computation

Understand secret sharing, garbled circuits, and MPC applications

Compute on encrypted data without revealing it

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Key Takeaways

You've learned how Multi-Party Computation enables trustless collaborative computing with cryptographic privacy guarantees. Let's review the key concepts before testing your knowledge.

🔐 MPC Fundamentals

MPC solves the trust problem: multiple parties can compute a function f(x₁, x₂, ..., xₙ) without revealing private inputs. It eliminates the need for trusted third parties through cryptographic protocols.

  • •Privacy guaranteed: parties learn only the output, nothing about others' inputs
  • •Correctness guaranteed: output is mathematically correct even with malicious parties
  • •Decentralized trust: no single party can compromise security

📐 Secret Sharing (Shamir)

Foundation of MPC: split secret into n shares with threshold t such that any t shares reconstruct the secret, but t-1 shares reveal zero information.

  • •Perfect secrecy: t-1 shares = 0% information (information-theoretic security)
  • •Perfect reconstruction: t shares uniquely determine the secret
  • •Homomorphic: [a] + [b] = [a+b], c × [a] = [c×a] enables computation on shares
  • •Common setups: 2-of-3 (wallets), 3-of-5 (institutions), 5-of-9 (DAOs)

⚡ Secure Computation Protocols

Different protocols offer different tradeoffs for computing on secret-shared data:

  • •GMW (1987): Boolean circuits, O(depth) rounds, general-purpose
  • •Yao's GC (1986): Constant rounds (2), 2-party only, low latency
  • •SPDZ (2012): Arithmetic circuits, malicious security, preprocessing
  • •ABY3 (2018): Hybrid approach, 3-party optimal, ML-friendly
  • •Addition is free (local), multiplication requires communication (interactive)

🚀 Real-World Applications

MPC is production-ready and deployed across multiple industries:

  • •Blockchain: Threshold signatures (Fireblocks, Coinbase), private txs (Aztec), bridges (THORChain)
  • •Finance: Fraud detection, AML analysis, credit scoring without sharing customer data
  • •Healthcare: Multi-hospital research, genome analysis, drug discovery with patient privacy
  • •ML: Federated learning (Google, Apple), private inference, collaborative AI training
  • •Performance: AES in ~50ms, ECDSA in ~500ms, NN inference in ~2s (2024 benchmarks)

💡 Key Insights

  • •MPC proves privacy and utility are not opposites—you can have both
  • •Security models: semi-honest (passive) vs malicious (active) vs honest majority
  • •Tradeoffs: rounds vs communication, arithmetic vs boolean circuits, preprocessing vs online
  • •Frameworks: MP-SPDZ (general), CrypTen (PyTorch), TF Encrypted (TensorFlow)
  • •Performance improved 1000x in last decade—practical for production use
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