Roseman Labs is a Deep-Tech company from the Netherlands founded in 2020, with the mission to protect privacy and enable collaboration on sensitive data to address global challenges.

Our software uses patented multi-party computation to enable organizations to encrypt, link, and analyze multiple data sets quickly and securely, ensuring the privacy and commercial sensitivity of the data without exposing the underlying information. Our technology’s performance has earned us awards like the CES Innovation Award in Cybersecurity & Privacy and the 2024 Dutch Privacy Award. We also hold ISO 27001 and NEN7510 certifications, ensuring top-tier information security.

For more information on some of the challenges we solve, click here.

Multi-Party Computation (MPC) is a privacy enhancing technology that enables multiple parties to analyze data together without revealing the actual data to each other. It allows for calculations like averages and comparisons across different datasets while keeping the individual records in the data private. 

At its core, MPC encrypts and divides data at its source into 'secret shares'. These shares, which reveal nothing on their own, are then distributed across three servers that work together as an engine. The servers perform calculations on the secret shares, and only the final result is decrypted and shared with the analyst - assuming the analysis has been approved.

This ensures that sensitive data remains protected throughout the analysis process, providing a secure way to gain insights from multiple data sources without compromising privacy. 

Many common types of data analysis computations can be performed in the Roseman Labs engine, including more complex calculations like logistic and linear regression, as well as K-nearest neighbors. Our engine translates any Python code you write so that it can be executed without revealing the underlying data, thanks to the expertise of Roseman Labs’ cryptography team.  

For the full functionality of our Python package crandas, please refer to our documentation.

As part of our data request functionality, Roseman Labs includes robust data validation features. When a data request is initiated, the user can define validation rules such as specific column names, data types, and constraints like minimum and maximum values, required strings or the option to leave a column empty.

These validation checks are performed client-side, ensuring that the data meets the specified schema and rules before it is uploaded to our platform. This means the participant’s data is thoroughly validated on their device, maintaining privacy and ensuring quality without exposing the data.

The engine operates on three servers in different clouds (OVHcloud, Fuga, and Scaleway), each controlled by different cloud admins. Due to the Multi-Party Computation protocol used, a majority of these parties would need to collude to reveal the underlying sensitive data. It is mathematically proven that no data can be revealed as long as the parties operating the servers do not collude. Additionally, security is enhanced through our script approval process, ensuring that only approved analyses are executed on the sensitive data.