Opaque Systems announced that it has raised $22 million in Series A financing, bringing total financing to $31.6 million. The San Francisco-based company is focused on enabling collaborative analysis and AI in confidential computing and plans to use the funds to meet the growing market demand for this technology.
Confidential computing is an emerging cloud computing technology that isolates data within a protected CPU for in-memory processing. The CPU’s processes and resources are hidden from the rest of the system stack and can only be accessed through privileged access. Gartner predicts that by 2025, more than half of organizations will use confidential computers to process sensitive data.
There is a data access challenge when working with data owned by multiple parties, as sharing or analyzing it securely can be difficult or impossible. In a blog postt, Opaque Systems VP Product Jay Harel provides examples of use cases where this challenge arises: working together to identify and prevent money laundering in financial services, sharing confidential patient information for clinical trials, and sharing sensor data and manufacturing information to perform preventive maintenance applications to be performed. He also lists the critical data access needs of businesses, including end-to-end encryption and data protection, the ability to share and perform collaborative analytics across multiple parties, and compliance with regulatory policies for dating sharing.
The Opaque Systems platform is based on the open source confidential computing project MC2, created at UC Berkeley by the company’s five co-founders. The Opaque team is made up of esteemed security researchers and practitioners, including UC Berkeley professors Raluca Ada Popa and Ion Stoica (Co-founder of Databricks), as well as former UC Berkeley graduates and industry veterans Rishabh Poddar, Wenting Zheng and Chester Leung.
The team’s goal for the Opaque Platform launch was to build on MC2 for an enterprise-centric platform that allows users to share encrypted datasets across multiple teams and organizations for collaborative analytics or AI/ML development, all while preserving specific confidentiality protocols. According to the company, encrypted data is never made public and analytic results are kept private for each party.
“The Opaque Platform allows you to perform analytics and ML at scale on encrypted data, while securely collaborating within and across organizational boundaries,” says Harel. “Our platform allows you to upload encrypted data or connect to various encrypted sources. You can then edit and run high-performance SQL queries, analysis tasks, and AI/ML models using well-known notebooks and analytic tools.”
The Opaque Platform uses multiple layers of security reinforced with cryptographic techniques and uses only NIST-approved encryption. The platform can scale multidimensionally across enclaves, data sources and multiple parties with secure access across enclave clusters. The company states that users can also automate cluster orchestration, monitoring, and management across multiple workspaces without operational disruption.
Investors look ready to jump on the confidential computer wave that research firm Everest Group estimates could be worth $54 billion by 2026. The platform’s latest funding round was led by Walden Catalyst Partners, with the participation of new investors, Storm Ventures and Thomvest Ventures, as well as all existing investors, Intel Capital, Race Capital, The House Fund, and FactoryHQ.
“Our new investors, Walden Catalyst, Storm Ventures and Thomvest Ventures, and existing investors see the tremendous market opportunity in performing collaborative analytics and AI on confidential data,” said Raluca Ada Popa, president and co-founder of Opaque Systems. “This funding will accelerate R&D and hiring as we strengthen Opaque’s position as the authority on multi-party analytics and AI for confidential computing. Global organizations urgently need a secure solution to collectively analyze their confidential data and we are well positioned to meet this growing demand.”
Security, privacy and governance at the data intersection in ’22’
How Unified Data Access Governance will determine the winners and losers in the new data economy
Revolutionize data collaboration with Federated Machine Learning