Multi-party computation is a cryptographic technique used in private AI that allows several parties to compute a shared result without revealing their private inputs. It protects data during use and is often applied when organizations need to collaborate without exposing sensitive information. 

Tools that support this technique include Sharemind, used in healthcare research to analyze patient data across hospitals, and Unbound Security, which has been applied in retail for secure collaborative analytics and forecasting.

It belongs to the category of privacy-enhancing technologies, which protect information during processing. A related approach is homomorphic encryption, where encrypted data can be used in calculations. Multi-party computation offers an alternative by splitting data across parties and keeping each part hidden, reducing the risk of exposure during analysis.

The process involves dividing each input into encrypted or secret shares and distributing them across the participants. Each party processes only the portion it receives, and the combined computation yields a correct result. Throughout, no participant gains access to the complete input of any other party.

How does multi-party computation work? 

Multi-party computation allows several parties to work together on a shared calculation without exchanging their raw data. Here is an overview of how it works:

Define the goal and inputs

Before the process begins, all parties agree on what needs to be calculated, such as comparing treatment outcomes in healthcare or detecting overlapping customer segments in retail. Each participant prepares their own input, such as patient data or sales records, but does not share it directly with others.

Encode data for privacy

The raw data is converted into a protected form. Common methods include encryption or secret sharing, where individual inputs are split into fragments. Each fragment alone carries no usable information.

Distribute shares or encrypted values

Each participant receives only the protected values needed for their role in the process. In a secret sharing scheme, no single party holds enough information to reconstruct any original input.

Compute across parties securely

The calculation takes place across the distributed data. Each party processes only the information they hold, and the protocol ensures the correct result is only produced when all participants follow the required steps.

Reveal only the final output

When all participants complete their part correctly, the system produces the agreed outcome. The result is shared with participants, while the original inputs remain private. No further data is exposed beyond what the result implies.

What are multi-party computation techniques?

Several distinct techniques can be used to implement multi-party computation. The methods below form the basis of many enterprise-grade privacy solutions:

Secret sharing

Secret sharing splits a value into separate parts and distributes them across participants. No part reveals the full input. The original value can only be recovered when a set number of parts are combined. This method is widely used in finance and healthcare where data must remain hidden unless access is explicitly granted.

Threshold cryptography

Threshold cryptography applies the principle of shared control to cryptographic keys. Instead of storing a complete private key in one place, organizations divide the key across multiple systems, parties or secure environments like trusted execution environments. Only a defined group can work together to complete secure actions such as signing a transaction or accessing a system.

Oblivious transfer

Oblivious transfer allows one party to retrieve a value from another without revealing which value was chosen. The sender remains unaware of the request, and the receiver learns only the selected item. Organizations use this technique for scenarios such as confidential audits and private database lookups.

Garbled circuits

Garbled circuits let two parties compute a result without exposing their inputs. One encrypts the logic, and the other runs the computation without uncovering any underlying data. Enterprises use the method for private benchmarking or salary comparisons.

Private set intersection

Private set intersection identifies common values between datasets while keeping the rest of the data hidden. Organizations in sectors like healthcare or retail use this method to find shared records, such as overlapping patients or loyalty members, without disclosing unrelated information.

What are some common multi-party computation use cases? 

Multi-party computation helps organizations work with private data across boundaries they cannot cross with direct sharing.

Collaborative patient outcome analysis

Hospitals and research centers often study treatment effectiveness using techniques like federated learning. A single site may lack sufficient data, but with MPC, multiple institutions can run shared analyses across local datasets. Each prepares and processes its data without exposing patient details. The final output reveals group-level insights while preserving privacy. For example, hospitals in Germany and Italy used MPC to study cancer treatment outcomes.

Secure cross-retailer demand forecasting

Retailers serving similar markets may want to forecast demand trends collaboratively but can’t share store-level data. With MPC, each contributes protected values to a joint model. The result reflects market-wide shifts without revealing individual performance. Each retailer retains control of its data and learns only the aggregated trend.

Privacy-preserving credit risk assessment

Banks may serve overlapping customers but can’t access each other’s records. To assess sector-wide risk, each bank shares privacy-protected attributes — such as hashed customer IDs or risk indicators. MPC compares these values without revealing underlying data. The result highlights shared exposure while maintaining confidentiality.

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