Opaque Systems Helps Enterprises Perform Collaborative Analytics on Confidential Data

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

    Confidential computing is a game-changer for enterprises. It encrypts sensitive data in a protected CPU enclave or trusted execution environment (TEE), giving businesses a way to go beyond policy-based privacy and security to protect their information in the cloud. With this level of encryption, which can only be unlocked with keys owned by the customer, multiple parties struggle to access, share, analyze and execute AI/ML on the data in question. Imagine data scientists and analysts from different teams wanting to access patient data to improve different aspects of care.

    The Opaque Systems platform

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

    “What’s unique is the innovation we’ve done in Spark, which allows the analytics and AI to run directly on encrypted data, so whether the data is at rest, in transit, or in processing, there’s absolutely no exposure or risk that the data is exposed to entities that should not see it, possess 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, which started at UC Berkeley to enable collaborative analysis and AI on confidential and sensitive encrypted data. It allows companies to share the encrypted or blended datasets with workspaces and teams (with established policies) for analysis, while keeping the encrypted results specific to each party. In this way, multiple teams can build a distributed model that informs each party about what they are looking for without ever revealing specific data that the entity is not allowed to see.

    Since its launch, Opaque Systems has seen demand from all industries for use cases such as money laundering, collaborative drug discovery, loan piling prevention, and supply chain tracking.

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

    Resource-heavy alternatives

    Many enterprises rely on homomorphic encryption, which converts data into ciphertext, and multiparty computation to perform analysis on encrypted data without compromising encryption. The methods, Poddar says, work, but come with high resource consumption and performance overhead.

    “Through extensive research, we’ve seen that these technologies are far from practical for the scalable, highly secure data analytics and machine learning needed to execute critical business cases. Some of these solutions can handle simple computations but quickly become prohibitive in performance for scalable data analysis and ML training,” he added.

    With this funding round, led by Walden Catalyst Ventures, Opaque Systems will focus on expanding its team and building its offerings to meet increasing market demand for collaborative analytics and AI in confidential computing. According to Gartner, more than 50% of organizations will adopt privacy-enhancing computations by 2025 to process sensitive data and perform multi-party analytics.

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