Agentic DesignPatterns and tools for production AI agents

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Secure Multi-Party Computation(SMPC)

Privacy-preserving collaboration between multiple agents without revealing private data

Complexity: highSecurity & Privacy Patterns

In 30 seconds

What: Multiple agents compute results on encrypted data without any party seeing others' inputs, using secret sharing and homomorphic encryption.
When to use: Competing organizations need to collaborate on analysis (fraud detection, risk assessment) while keeping sensitive data completely private.
Watch out: Cryptographic overhead makes this extremely slow; only viable for low-frequency, high-stakes computations, not real-time systems.

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Agentic DesignPatterns and tools for production AI agents

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Design Patterns & Techniques

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🔐

Secure Multi-Party Computation(SMPC)

Privacy-preserving collaboration between multiple agents without revealing private data

Complexity: highSecurity & Privacy Patterns

In 30 seconds

What: Multiple agents compute results on encrypted data without any party seeing others' inputs, using secret sharing and homomorphic encryption.
When to use: Competing organizations need to collaborate on analysis (fraud detection, risk assessment) while keeping sensitive data completely private.
Watch out: Cryptographic overhead makes this extremely slow; only viable for low-frequency, high-stakes computations, not real-time systems.

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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Patterns

Design Patterns & Techniques

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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)

Secure Multi-Party Computation (SMPC) - Agentic Design