Opaque Systems Unveils Its First Multiparty Confidential AI and Analytics Platform
Addressing the rising data privacy requirements and regulations, the Opaque Platform provides breakthroughs in secure sharing of and collaborative analytics and AI on encrypted data.
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Opaque Systems, providers of secure multiparty analytics and AI for confidential computing, has announced its latest advancements in confidential AI and analytics with the unveiling of its platform. The Opaque Platform, built to unlock use cases in confidential computing, is created by the inventors of the popular MC2 open source project, which was conceived 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 trusted execution environments (TEEs). The platform further accelerates confidential computing use cases by enabling data scientists to use their existing SQL and Python skills to run 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.
Confidential computing provides a solution using TEEs or “enclaves” that encrypt data during computation, isolating it from access, exposure, and threats. However, TEEs have historically been challenging for data scientists due to the restricted access to data, lack of tools that enable data sharing and collaborative analytics, and the highly specialized skills needed to work with data encrypted in TEEs. The Opaque Platform overcomes these challenges by providing the first multiparty confidential analytics and AI solution that makes it possible to run frictionless analytics on encrypted data within TEEs, enables secure data sharing, and enables multiple parties to perform collaborative analytics while ensuring each party only has access to the data they own.
The Opaque Confidential AI and Analytics Platform is designed to ensure that both code and data within enclaves are inaccessible to other users or processes that are collocated on the system. An organization can encrypt its confidential data on premises, accelerate the transition of sensitive workloads to enclaves in confidential computing clouds, and analyze encrypted data while ensuring it is never unencrypted during the life cycle of the computation. Key capabilities and advancements include:
- Secure, multiparty collaborative analytics. Multiple data owners can pool their encrypted data together in the cloud and jointly analyze the collective data without compromising confidentiality. Policy enforcement capabilities ensure the data owned by each party is never exposed to other data owners.
- Secure data sharing and data privacy. Teams across departments and across organizations can securely share data protected in TEEs while adhering to regulatory and compliance policies. Use cases requiring confidential data sharing include financial crime, drug research, ad targeting monetization, and more.
- Data protection throughout the life cycle. Protects all sensitive data, including PII and SHI data, using advanced encryption and secure hardware enclave technology, throughout the life cycle of computation—from data upload to analytics and insights.
- Multitiered 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 side-channel attack protection.
- Scalability and orchestration of enclave clusters. Provides distributed confidential data processing across managed TEE clusters and automates orchestration of clusters overcoming performance and scaling challenges and supports secure inter-enclave communication.
Confidential computing is supported by all major cloud vendors including Microsoft Azure, Google Cloud, and Amazon Web Services, and major chip manufacturers including Intel and AMD.
To learn more, visit https://opaque.co