San Francisco, California December 8, 2022 – Opaque systemspioneers of secure multi-party analytics and AI for confidential computing, announced the latest advancements in Confidential AI and Analytics with the unveiling of its platform. The Opaque platform, designed to unlock use cases in confidential computing, is created by the inventors of the popular MC2 open-source project which was designed in the RISELab at UC Berkeley. The opaque platform enables data scientists within and across organizations to securely share data and perform collaborative analytics directly on encrypted data protected by secure execution environments (TEEs). The platform further accelerates confidential computing use cases by allowing data scientists to leverage their existing SQL and Python skills to perform analytics and machine learning while working with confidential data, overcoming the data analytics challenges inherent in TEEs due to their strict protection of how data is accessed. and used. Opaque platform advancements come on the heels of Opaque announcing its Series A funding of $22 million,
Confidential Computing – which is expected to be a $54 billion market by 2026 according to the Everest Group – provides a solution using TEEs or “enclaves” that encrypt data during computation, isolating it from access, exposure and threats. However, TEEs have always been challenging for data scientists due to restricted access to data, lack of tools to enable data sharing and collaborative analysis, and highly specialized skills needed to work with data. quantified in the TEEs. The opaque platform overcomes these challenges by providing the first multi-party privacy and AI analytics solution that enables frictionless analytics to be run on encrypted data in TEEs, enables secure data sharing and, for first time, to enable multiple parties to perform collaborative analyses. while ensuring that each party only has access to the data it owns.
“Traditional approaches to data protection and data privacy management leave data exposed and at risk when processed by applications, analytics and machine learning (ML) models,” said Rishabh Poddar, co-founder and CEO of Opaque Systems. “The Opaque Confidential AI and Analytics Platform solves this problem by enabling data scientists and analysts to perform scalable and secure analytics and machine learning directly on encrypted data in enclaves to unlock cases. use of confidential computing.”
“Strict privacy regulations make sensitive data difficult to access and analyze,” said a data science manager at a major US bank. “The new secure multi-party analytics and computational capabilities and privacy-enhancing technology from Opaque Systems will dramatically improve the accuracy of AI/ML/NLP models and accelerate insights.”
The opaque confidential AI and analytics platform is designed to specifically ensure that code and data in enclaves are inaccessible to other users or processes that are co-located on the system. Organizations can encrypt their confidential data on-premises, accelerate the transition of sensitive workloads to confidential computing cloud enclaves, and analyze encrypted data while ensuring it is never decrypted during the compute lifecycle . Key features and advancements include:
- Secure, multi-stakeholder collaborative analytics – Multiple data owners can aggregate their encrypted data in the cloud and jointly analyze the collective data without compromising privacy. Policy enforcement capabilities ensure that data owned by each party is never exposed to other data owners.
- Secure data sharing and data privacy – Teams across departments and organizations can securely share protected data in TEEs while adhering to regulatory and compliance policies. Use cases requiring the sharing of confidential data include financial crime, drug research, ad targeting monetization, and more.
- Lifecycle data protection – Protects all sensitive data, including PII and SHI data, using advanced encryption and secure hardware enclave technology, throughout the compute lifecycle, from data download to storage. analysis and information.
- Multi-level security, policy enforcement and governance – Leverages multiple layers of security, including Intel® Software Guard extensions, secure enclaves, advanced cryptography and policy enforcement to provide defense in depth, ensuring code integrity, data and protection against side channel attacks.
- Enclave cluster scalability and orchestration – Provides distributed processing of confidential data across managed TEE clusters and automates cluster orchestration by overcoming performance and scaling issues and supporting secure inter-enclave communication.
Confidential Computing is supported by all major cloud providers, including Microsoft Azure, Google Cloud, and Amazon Web Services, as well as major chipmakers, including Intel and AMD.
To learn more about Opaque’s confidential AI and analytics platform, visit www.opaque.co. For other confidential computing industry updates and insights, follow Opaque Systems on Twitter @opaquesys and on LinkedIn at https://www.linkedin.com/company/opaquesystems/. Learn more about confidential and collaborative analytics at https://opaque.co/blog/.
About Opaque Systems
Commercializing the open-source MC2 technology invented at UC Berkeley by its founders, Opaque System provides the first collaborative analytics and AI platform for confidential computing. Opaque uniquely enables data to be shared and analyzed securely by multiple parties while maintaining complete privacy and end-to-end data protection. The opaque platform leverages a new combination of two key technologies layered with industry-leading cloud security: secure hardware enclaves and cryptographic fortification. This combination ensures that the overall computation is secure, fast, and scalable. MC2 technology and Opaque innovation have already been adopted by several organizations, such as Ant Group, IBM, Scotiabank and Ericsson. For more information on opaque systems, please visit https://opaque.co/.