Opaque systems help businesses perform collaborative analytics on confidential data

San Francisco-based Opaque Systems, a company that enables collaborative analytics and AI for confidential computing, announced today that it has raised $ 22 million in a Series A round of funding.

Confidential computers were a game changer for businesses. It encrypts sensitive data in a protected CPU enclave or trusted execution environment (TEE), which provides companies with a way to move beyond policy-based privacy and security to protect their information in the cloud. However, with this level of encryption, which can only be unlocked with client-held keys, multiple parties struggle to access, share, analyze, and run AI / ML on the relevant data. Imagine data scientists and analysts from different teams seeking access to patient data to improve different aspects of care.

Opaque Systems’ platform

To solve this challenge, Opaque Systems offers a platform that performs scalable analysis and machine learning directly on encrypted data using well-known tools such as Apache Spark and notebooks.

“What is unique is the innovation we have done in Spark that enables the analysis and AI to run directly on encrypted data, so whether data is at rest, in transport or processed, there is absolutely no exposure or risk that the data is not exposed to entities that should not see it, own it or malicious actors. Opaque integrates seamlessly with TEEs, including enclaves and confidential VMs, with the ability to securely scale clusters, ”Rishabh Poddar, co-founder and CEO of Opaque Systems, told VentureBeat.

The enterprise-focused platform builds on the open-source MC2 initiative, launched at UC Berkeley to enable collaborative analytics and AI on confidential and sensitive encrypted data. It enables companies to share the encrypted or mixed datasets on workspaces and teams (with established policies) for analysis – while retaining the encrypted results specifically for each party. This way, multiple teams can build a distributed model that informs each party about what they are looking for without ever disclosing any specific data that the entity is not authorized to see.

Since its launch, Opaque Systems has seen demand from across sectors for use cases such as money laundering, collaborative drug research, loan stacking prevention and supply chain tracking.

“Our customer base is primarily Global 2000s, including several of the largest banks, financial institutions and healthcare providers in North America. Customers also include consortia, as many of our use cases are multiparty, so that means one customer in turn 3-4 separate entities or discrete organizations, ”Poddar said.

Resource-heavy alternatives

Many businesses rely on homomorphic encryption, where data is converted into numeric text, and multi-party computing to perform analysis on encrypted data without compromising the encryption. The methods, says Poddar, do work, but are also associated with high resource consumption and performance overhead.

“Through extensive research, we have seen that these technologies are far from practical for scalable, highly secure data analysis and machine learning needed to execute critical business cases. Some of these solutions can support simple calculations, but they quickly become unaffordable in performance for scalable data analysis and ML training, ”he added.

With this round of funding, led by Walden Catalyst Ventures, Opaque Systems will focus on expanding its team and expanding its offering to meet the accelerated market demand for collaborative analytics and AI in confidential computers. According to Gartner, by 2025, more than 50% of organizations will use privacy-enhancing calculations to process sensitive data and perform multi-party analysis.