Agentic Design

Patterns
🔐

Secure Multi-Party Computation(SMPC)

Privacy-preserving collaboration between multiple agents without revealing private data

Complexity: highSecurity & Privacy Patterns

🎯 30-Second Overview

Pattern: Privacy-preserving collaboration between multiple agents without revealing private data through cryptographic protocols

Why: Enables secure data collaboration, maintains competitive advantages, ensures regulatory compliance, and builds trust

Key Insight: Secret sharing + homomorphic computation + cryptographic proofs → secure multi-party analysis

⚡ Quick Implementation

1Secret Sharing:Distribute data across multiple parties
2Protocol Setup:Establish cryptographic protocols
3Private Computation:Execute operations on encrypted data
4Result Aggregation:Combine partial results securely
5Verification:Cryptographic proof of correctness
Example: secret_sharing → protocol_setup → private_computation → result_aggregation → verification

📋 Do's & Don'ts

✅Use proven cryptographic protocols (Shamir, BGW, GMW)
✅Implement secure channels for all party communications
✅Verify computational integrity with cryptographic proofs
✅Plan for party dropout and recovery mechanisms
✅Use threshold schemes to prevent single points of failure
❌Trust parties to follow protocols without verification
❌Ignore performance implications of cryptographic overhead
❌Use insecure random number generation for secrets
❌Skip security analysis for custom protocol modifications
❌Assume all parties have equal computational resources

🚦 When to Use

Use When

  • • Multi-organization data analysis
  • • Competitive intelligence sharing
  • • Privacy-preserving federated learning
  • • Regulatory compliance requirements

Avoid When

  • • Single-party data processing
  • • Public data computations
  • • Real-time low-latency requirements
  • • Simple aggregation tasks

📊 Key Metrics

Privacy Preservation
Zero data leakage guarantee
Computation Accuracy
Correctness of results vs. plaintext
Protocol Efficiency
Communication rounds and bandwidth
Scalability
Performance with increasing parties
Security Proofs
Cryptographic security guarantees
Fault Tolerance
Resilience to party failures

💡 Top Use Cases

Banking Consortium: Joint fraud detection without sharing customer data
Healthcare Research: Multi-hospital studies preserving patient privacy
Supply Chain: Collaborative analytics without revealing trade secrets
Ad Tech: Privacy-preserving audience measurement and attribution
Government: Inter-agency intelligence sharing with classification protection

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

Secure Multiparty Computation: Theory and Practice (Evans et al.)

BGW Protocol - Secure Multiparty Computation (Ben-Or et al., 1988)

GMW Protocol - How to Play Any Mental Game (Goldreich et al., 1987)

Practical Secure Computation Survey (Lindell & Pinkas, 2009)

Implementation Guides

Microsoft SEAL - Homomorphic Encryption Library

Google Private Join and Compute

Facebook CrypTen - Secure Multi-Party ML

IBM Federated Learning - Privacy Preserving ML

Tools & Libraries

MP-SPDZ - Versatile MPC Framework

ABY Framework - Mixed Protocol MPC

MOTION - Mixed-Protocol Framework

PySyft - Privacy Preserving ML

Community & Discussions

IACR ePrint Archive - Cryptography Papers

Real World Crypto Symposium

OpenMined Community - Privacy ML

Privacy Preserving ML Workshop (PPML)

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