What Is Secure Multiparty Computation (MPC)? A Comprehensive Introduction

As data privacy becomes a top priority in sectors like finance, healthcare, and government, technologies that enable secure collaboration without compromising sensitive information are becoming essential. One such technology is Secure Multiparty Computation (MPC).

Drawing from Yehuda Lindell’s foundational article and real-world deployment examples like Inpher, this article provides a thorough introduction to what MPC is, how it works, and why it’s a transformative tool in modern data security.

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What Is Secure Multiparty Computation (MPC)?

Secure Multiparty Computation (MPC) is a cryptographic technique that allows multiple parties to jointly compute a function over their private inputs without revealing those inputs to one another.

In simple terms: MPC lets organizations “compute together” without having to “share data.”

Example:

Three banks want to determine if a client is committing fraud by looking at transaction patterns across all banks—but none of them is willing (or legally allowed) to share raw transaction data. MPC enables them to run a fraud-detection algorithm without any bank ever seeing the others’ data.

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Core Goals of MPC

According to Lindell, MPC protocols are designed to ensure two key properties:

1. Privacy

No party learns anything about other parties’ inputs, except what can be inferred from the output.

2. Correctness

The output is guaranteed to be correct, even if some participants try to cheat—depending on the model used.

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Security Models in MPC

There are two main adversarial models used when designing MPC protocols:

• Semi-Honest Model: Parties follow the protocol but try to learn extra information from the messages.
• Malicious Model: Parties may deviate from the protocol in any way. Protocols secure in this model provide stronger guarantees but are usually more complex and slower.

The choice depends on trust assumptions and threat levels.

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How Does MPC Work?

MPC relies on several foundational techniques in cryptography:

• Secret Sharing: Splitting data into multiple shares so no single party holds the full secret.
• Oblivious Transfer: Enables one party to send data without knowing what the other party received.
• Homomorphic Encryption: Allows computation on encrypted data without decryption.

Together, these allow participants to compute securely over distributed data without ever seeing the full dataset.

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Real-World Application: Inpher

Inpher is a privacy-preserving computation company that implements MPC for secure enterprise analytics.

Use Cases:

• Banking: Performing joint risk modeling or credit scoring across institutions without sharing personal financial data.
• Healthcare: Running machine learning models on patient records held at different hospitals without exposing sensitive health data.
• Government: Coordinating cross-border security or intelligence analysis without data centralization.

Their implementation of MPC supports encrypted data analysis, enabling teams to collaborate across data silos while remaining compliant with regulations like GDPR, CCPA, and HIPAA.

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Advantages of MPC

• ✅ Data remains confidential—no central storage or exposure
• ✅ Enables collaboration across competitors or jurisdictions
• ✅ Compliant with strict privacy laws
• ✅ No trusted third party required

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Challenges and Considerations

• Performance: Secure protocols are more resource-intensive.
• Complexity: Implementation requires expert cryptographic engineering.
• Verification: Ensuring correctness and auditability across multiple parties can be complex.

Despite these, recent advancements in MPC (e.g., preprocessing techniques, optimized protocols) are making real-time, scalable applications increasingly practical.

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Conclusion

Secure Multiparty Computation is revolutionizing how organizations can collaborate on sensitive data. By allowing joint computation without data exposure, MPC creates new possibilities for cross-entity analytics, federated machine learning, and privacy-first innovation.

As companies like Inpher lead the charge in real-world deployment, and as cryptographic research (like Lindell’s work) continues to mature, MPC is becoming a core pillar of the modern privacy stack.