Confidential
Compute
Compute insights across sensitive datasets without exposing underlying data, enabling compliant analytics, research, and cross-organisational collaboration.
What Is Confidential Data Computation ?
Confidential computation allows multiple parties to compute results from sensitive data without revealing individual inputs or raw datasets.
Practical Implementations
Enable secure research, analytics, voting, AML, healthcare studies, and collaborative decision-making while maintaining confidentiality, compliance, and verifiable correctness.
Confidential Compute Use Cases
Confidential Health Data Computation
Confidential health data computation enables researchers to analyse sensitive medical data without accessing or exposing individual patient records.
Problem: Healthcare researchers face significant barriers accessing patient data due to strict privacy requirements. Preparing datasets often requires costly anonymisation, lengthy approval processes, and manual controls, delaying research outcomes and limiting real-time analysis while still carrying residual re-identification risk.
Solution: Partisia Blockchain enables sensitive health data from multiple sources to be pre-processed and privatized within decentralised MPC clusters. Researchers can compute approved analyses across combined datasets without direct data access, reducing preparation costs, accelerating research timelines, and enabling real-time insights while preserving patient privacy.
Enhanced AML (Anti–Money Laundering)
Enhanced AML enables financial institutions to detect fraud collaboratively by analysing shared patterns across institutions without exposing confidential customer or transaction data.
Problem: Financial fraud is difficult to detect because banks cannot share sensitive customer or transaction data with each other. Fraudsters exploit this fragmentation using money mules, multiple accounts, and digital assets to obscure activity, making detection slow, incomplete, or impossible across isolated systems.
Solution: Partisia Blockchain enables financial institutions to compute AML analytics across collectively shared data using decentralised MPC. Each institution retains control of its data while approved algorithms run on combined inputs, allowing faster identification of fraud patterns, earlier intervention, and actionable outcomes without sharing raw or identifiable information.
Data Market 3.0
Data Market 3.0 enables trusted data exchange by allowing computation on user-controlled data without transferring or exposing the underlying information.
Problem: Traditional data markets suffer from untrustworthy data, misuse of personal information, and growing volumes of synthetic or manipulated content. Buyers struggle to verify data quality, while individuals must surrender control of sensitive information, creating privacy risks and eroding confidence in data-driven outcomes.
Solution: Data Market 3.0 uses decentralised MPC to let individuals retain ownership of their data while enabling approved computation on it. Human verification and confidential execution ensure buyers receive reliable insights without accessing raw data, restoring trust, improving data quality, and aligning incentives between data providers and consumers.
