MPC and ZKPs: Complementary Technologies for Secure, Verifiable Computation

In the evolving landscape of privacy-enhancing technologies, Multiparty Computation (MPC) and Zero-Knowledge Proofs (ZKPs) stand out as two foundational cryptographic tools. While often seen as addressing distinct privacy goals, they are increasingly being combined to achieve stronger guarantees of privacy, integrity, and trust in secure data processing.

This article explores the differences, complementarities, and combined use of MPC and ZKPs, and how they work together in modern applications such as secure finance, digital identity, and privacy-preserving AI.

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What Is a Zero-Knowledge Proof?

A Zero-Knowledge Proof (ZKP) is a cryptographic technique that allows one party (the prover) to prove to another (the verifier) that a statement is true without revealing any information beyond the validity of the statement itself.

Everyday Analogy:

Proving you know the key to a locked treasure chest by unlocking it, without showing the key.

ZKPs validate knowledge without exposure—a key tool for authentication, compliance, and trust.

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MPC vs. ZKP: Different Goals, Different Tools

Feature	Multiparty Computation (MPC)	Zero-Knowledge Proofs (ZKPs)
Primary goal	Secure collaborative computation	Verifiable claims without revealing inputs
Used for	Private data analysis, joint metrics	Identity, proof of validity, correctness
Data exposure	Inputs stay hidden across computation	Inputs remain hidden but claim is provable
Example use	Computing an average without sharing data	Proving you know your password without revealing it

MPC enables private joint computation, while ZKPs ensure truth without transparency—ideal for settings where validation without disclosure is needed.

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Using MPC and ZKPs Together

Rather than viewing them as competitors, MPC and ZKPs can be combined to achieve:

✅ Input Validity with Privacy

Use ZKPs to prove inputs are valid (e.g., within a required range or well-formed) before feeding them into an MPC protocol.

Example: Banks submitting financial data prove via ZKP that their numbers are within regulatory bounds—without revealing the values.

✅ Collusion Resistance

ZKPs can reduce the risk of collusion in MPC by preventing malicious parties from submitting manipulated data or gaining insights via collaboration.

✅ Verifiable Computation

Use MPC to compute a result without revealing inputs, and then apply a ZKP to prove the computation was correct without exposing intermediate steps or raw data.

Example: A joint credit risk score is computed via MPC, then verified publicly using a ZKP, maintaining both privacy and transparency.

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Real-World Applications

Privacy-Preserving Finance

• Use case: Competing banks compute a joint risk score.
• MPC ensures inputs (e.g., assets, liabilities) stay private.
• ZKP verifies that inputs were valid and results correctly computed.

Digital Identity

• ZKP: Prove you’re over 18 without revealing your birthdate.
• MPC: Aggregate multiple identity claims securely (e.g., passport + bank KYC).

Secure AI Inference

• MPC: Run ML inference on private user data.
• ZKP: Prove the model was applied correctly without showing the model or inputs.

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Benefits of Combining MPC with ZKP

• Stronger security guarantees
• Verifiability without sacrificing privacy
• Flexibility for complex, regulated systems
• Trust without central authority or full disclosure

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Conclusion: Better Together

MPC and ZKPs are not redundant—they are complementary. While MPC allows secure computation without revealing data, ZKPs allow you to prove truth without revealing why. Used together, they enable systems that are both private and verifiable, ideal for critical sectors like finance, digital governance, and healthcare.

As secure computation becomes a pillar of privacy-first technology, MPC + ZKP is becoming a gold standard for building trust without compromising confidentiality.