Multi-party computation is trending for digital ID privacy: Partisia explains why

Imagine being in a group of coworkers and trying to guess the average salary among you. The classical way of computing that figure would be adding all the salaries and dividing them by the number of coworkers. But some of your colleagues may not be so keen to reveal how much they pay.

This is where multi-party computation (MPC) comes in, a cryptographic technique that allows multiple parties to jointly compute a function without revealing the inputs to each other. The technique is now being adopted in the digital identity industry, where it’s used to build verification systems by companies such as Partisia.

“What MPC does is give us a possibility, a capability, of doing calculations on encrypted data,” says Mark Medum Bundgaard, the company’s chief product officer, in an interview.

The technique could have many applications, including keeping biometric data even more secure, and the Denmark-based firm has been testing its possibilities in Japan.

Earlier this year, the company teamed up with Japanese conglomerate Toppan and the Okinawa Institute of Science and Technology (OIST) to run a proof-of-concept (PoC) experiment for decentralized IDs verified with biometrics.

The trio is developing a student digital ID that combines Toppan Edge’s facial recognition with Partisia’s decentralized technology and meets eIDAS2.0 standards. The face biometrics are bound with information on an individual, such as name, address and age, and stored encrypted on a device. The test is running from June to September 2025

“That’s what we use MPC for, both for storing the face but potentially also for matching the biometrics,” Bundgaard told Biometric Update.

The main idea is achieving fully decentralized data, even biometric information, giving individuals even more privacy.

“We take their identity structure and we actually run the matching of the identity inside MPC,” he says.

This means that neither Partisia nor the company that runs the structure has the full biometric information. They can match it without ever decrypting it, Bundgaard explains.

Partisia says it’s getting close to this goal in its Japan experiment. The company has also been working on a similar goal of linking digital credentials to biometrics with U.S.-based Trust Stamp. But it is also developing other identity-related uses, such as proving age or other information.

In this case, documents from trusted issuers, such as diplomas, visas or IDs proving age, are encrypted. When another party wants to check whether an individual has a degree, a valid visa, or whether they are over 18, they only see a “Yes” or “No” without revealing the original document. Here, Partisia uses multiparty computation to store encrypted data in such a way that other parties can have access to some of the data, Bundgaard explains.

The firm is also hoping to bring its multiparty computation solutions to other sectors, including healthcare, travel and finance.

In Japan, it’s working with fintech company Digital Platformer on a platform that allows secure sharing of data among multiple banks. In Colombia, it’s looking into identity that is transferable to different locations and could potentially be used in travel. And in its home country of Denmark, the company is collaborating with the state statistics office to link the country’s healthcare database with socioeconomic data, allowing scientists to gain new insights on the health of Danes.

The project, called OSCAR, relies on Partisia’s Confidential Computing platform to collect, anonymize, merge, analyze and share often sensitive health data from public registries.

“I have some database somewhere that contains all my private healthcare data, and another database that contains statistics,” says Bundgaard. “No one is actually allowed to look concurrently at these things at the same time. So we need a way of calculating on this.”

Multiparty computation protocols are closing that gap: Since all data is encrypted, no one learns anything they did not already know. Beyond protecting data, another advantage is that it still allows data analysts to run computations on encrypted data, according to Partisia.

There may be another important role for this cryptographic technique when it comes to privacy. Blockchain and multiparty computation could potentially help lessen friction between European privacy standards, such as eIDAS and GDPR, and those of other countries.

“I have one standard in Japan and I travel to Europe and there is a different standard,” says Bundgaard. “I want some way of conforming to that standard, but not give my data away. So I could do it inside this MPC structure.”

Article Topics

biometrics  |  cryptographic patterns  |  data protection  |  decentralized biometrics  |  digital ID  |  multi-party computation  |  Partisia  |  Toppan Edge  |  Trust Stamp