Roseman Labs provides a platform for decentralized data analytics using Multi-Party Computation, allowing organizations to securely link and analyze sensitive datasets without exposing raw data. This technology enables compliance with GDPR while facilitating data-driven insights across healthcare and public sectors, enhancing decision-making and operational efficiency.
Funding
$4.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations often struggle to collaborate on sensitive data analysis due to privacy regulations like GDPR and the risk of exposing raw data. Traditional data analytics methods require sharing or centralizing datasets, creating security vulnerabilities and hindering data-driven insights.
Solution
Roseman Labs provides a secure, encrypted computing platform that enables organizations to link and analyze sensitive datasets without revealing the underlying raw data. Using Multi-Party Computation (MPC), the platform allows multiple parties to collaborate on data analysis while maintaining data confidentiality and complying with privacy regulations. This technology creates encrypted data spaces where organizations can combine their data with that of others, enrich existing datasets, and analyze millions of records to discover new patterns and train AI models, all without disclosing the raw input. Roseman Labs facilitates data-driven collaboration across healthcare, security, defense, and other sectors, enhancing decision-making and operational efficiency.
Target Audience
The primary target audience includes organizations in healthcare, security, defense, and the public sector that handle sensitive data and require secure, privacy-preserving data analytics and collaboration capabilities.
Features
- Encrypted data spaces for secure data combination and analysis
- Multi-Party Computation (MPC) to ensure data confidentiality during processing
- Privacy-preserving training of machine learning models
- Ability to link and enrich datasets with surveys and external sources
- Fast and accurate analysis of millions of records
- GDPR compliance through state-of-the-art encryption
- Python package (crandas) and developer documentation for easy integration
- SecureNed platform for cyber threat and incident information sharing