Today, organizations have vast amounts of confidential data that is locked down due to privacy concerns. Opaque Systems makes confidential data useful by enabling secure analytics and machine learning on encrypted data that comes from one or more sources.
With Opaque Systems, organizations can analyze encrypted data on the cloud using popular tools like Apache Spark, while ensuring that their data is never exposed unencrypted to the cloud provider.
Opaque Systems is powered by cutting-edge technology that builds upon years of published research by leading computer scientists at UC Berkeley. It commercializes the open source MC2 Platform, which leverages a novel combination of two key technologies — secure hardware enclaves and cryptographic fortification. This combination ensures that the overall computation is secure, fast, and scalable. MC2 has already seen adoption by several institutions, such as Ant Financial, IBM, Scotiabank, and Ericsson.
Confidential Cloud Computing
The Secure Computation in the Cloud solution enables organizations to migrate their sensitive data and models to the cloud. The solution ensures that the data is never exposed unencrypted on the cloud, unlocking the benefits of cloud computing while also guaranteeing data privacy, trust, and compliance. Organizations can upload encrypted data to the cloud and apply advanced analytics models or machine learning directly to the encrypted data.
Multiple data owners across different teams within an organization, or across different organizations, can employ Secure Collaboration in the Cloud to jointly analyze their collective data. The solution ensures that the data of individual owners is never exposed to either the cloud environment or to other data owners. Data owners individually upload encrypted data to the cloud, and then jointly apply analytics models directly on the encrypted data. Each data owner retains full control over how their data is used.
Opaque protects all Personally Identifiable Information (PII) using advanced encryption as well as secure hardware enclave technology, throughout the lifecycle of the analytics computation -- from data upload to applying the analytics models to obtaining the results. This enables organizations to avail the benefits of cloud computing while remaining compliant with the growing number of global privacy laws and regulations like GDPR and CCPA.
What we offer
Process structured data securely using Spark SQL. Run SQL queries, complex analytics, and ETL pipelines on data loaded from encrypted CSV files.
Securely train ML models over sensitive data, such as gradient boosted decision tree models using the XGBoost framework. Deploy trained models securely for classification and regression.
Reap the benefits of federated learning while significantly improving privacy guarantees via secure server-side aggregation of client updates.