Principles and Applications of Multi-Party Secure Computation (MPC) Technology in FinTech

Problem Description
Multi-Party Secure Computation (MPC) is a cryptographic technology that allows multiple participants to jointly complete a computational task without disclosing their respective private data. For example, multiple banks wish to jointly assess a customer's credit risk but are unwilling to directly share customer information. MPC uses mathematical protocols to ensure that each party can only obtain the final computation result and cannot infer the original data of others. This problem requires explaining the basic principles of MPC, typical algorithms (such as secret sharing and garbled circuits), and their practical application scenarios in FinTech.

Solution Process

1. Core Objectives and Basic Concepts of MPC

• Objective: Solve the "data silo" problem, enabling collaborative data computation under the premise of privacy protection.
• Concept: Transform the computation process into cryptographic protocols, using distributed computing and encryption techniques so that each party's input values always participate in operations in ciphertext form. For example, if two parties want to compare their salaries without revealing the exact amounts, an MPC protocol can output only the comparison result (e.g., "A's salary is higher") without disclosing either party's actual salary.

2. Technical Foundation of MPC: Secret Sharing

• Principle: Split the original data into multiple "shares" and distribute them to different participants. A single share cannot reconstruct the data, but multiple shares can collaboratively complete the computation.
  • Example (Addition Operation): A and B want to calculate the sum of their salaries but are unwilling to directly reveal them.
    • A splits their salary \(x\) into \(x_1, x_2\), such that \(x = x_1 + x_2\), sends \(x_1\) to B, and keeps \(x_2\).
    • B splits their salary \(y\) into \(y_1, y_2\), such that \(y = y_1 + y_2\), sends \(y_1\) to A, and keeps \(y_2\).
    • A calculates \(s_A = x_2 + y_1\), B calculates \(s_B = x_1 + y_2\). After exchanging \(s_A, s_B\), they sum them:

\[ s_A + s_B = (x_2 + y_1) + (x_1 + y_2) = (x_1 + x_2) + (y_1 + y_2) = x + y \]

- Throughout the process, A and B only see random shares and cannot infer the other's salary.  

3. Typical MPC Protocol: Garbled Circuits

• Applicable Scenarios: Nonlinear computations (e.g., comparisons, logical judgments).
• Steps (using a two-party comparison as an example):
  • Step 1: Transform the computational task into a Boolean circuit (e.g., a comparison circuit).
  • Step 2: One party (the generator) encrypts the circuit into a "garbled circuit," where the truth table of each logic gate is replaced with ciphertext labels.
  • Step 3: The other party (the evaluator) obtains labels corresponding to their input via oblivious transfer without knowing their meaning.
  • Step 4: The evaluator decrypts the garbled circuit gate by gate, finally obtaining the output label. The generator translates the label into the actual result and shares it.
• Key Point: The evaluator cannot associate intermediate labels with specific data, and the generator does not know the other party's input.

4. Application Cases of MPC in FinTech

• Joint Risk Control: Multiple banks use MPC to compute a customer's cross-institution debt ratio without sharing raw data.
• Anti-Money Laundering (AML): Financial institutions collaboratively screen blacklisted transactions while keeping their respective customer lists confidential.
• Quantitative Trading: Multiple investment institutions jointly train models, protecting the privacy of their respective strategy data.

5. Challenges and Limitations

• Computational Efficiency: Ciphertext operations are orders of magnitude slower than plaintext operations, requiring protocol optimization or hardware acceleration.
• Communication Overhead: Frequent multi-party interactions may affect real-time performance due to network latency.
• Security Assumptions: It is necessary to define the attack model (e.g., semi-honest or malicious models) and design corresponding verification mechanisms.

Summary
MPC combines data privacy with collaborative computation through cryptographic protocols, making it a key tool for addressing data silo problems in finance. Understanding its underlying mathematical principles (e.g., secret sharing) and typical protocols (e.g., garbled circuits) helps design privacy-preserving solutions tailored to actual business needs.