Confidential Computing Membership Grows 60 Percent within Nine Months of Formation
The Confidential Computing Consortium, a Linux Foundation project and community dedicated to defining and accelerating the adoption of confidential computing, today announced Accenture, AMD, Anjuna, Anqlave, Cosmian, iExec, IoTeX, NVIDIA, and R3 have joined as members to contribute to the adoption of Confidential Computing.
“In the past five months, we’ve added ten new members including a preeminent consultancy, a leading semiconductor manufacturer, and a pioneer in graphics processing,” said Stephen Walli, Governing Board Chair. “This is a brilliant group of innovative companies that has come together to solve one of the key challenges in information security; protecting applications and data while in use.”
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Today, data is often encrypted at rest in storage and in transit across the network, but not while in use in memory. Additionally, the ability to protect data and code is limited in conventional computing infrastructure. Organizations that handle sensitive data such as Personally Identifiable Information (PII), financial data, or health information need to mitigate threats that target the confidentiality and integrity of the applications and data in system memory.
Confidential Computing protects data in use by performing computation in a hardware-based Trusted Execution Environment. These secure and isolated environments prevent unauthorized access or modification of applications and data while in use, thereby increasing the security assurances for organizations that manage sensitive and regulated data. It protects applications and data from breaches, malicious actors and insider threats, while providing the portability to move sensitive workloads between on-premises data centers, public cloud and the edge.
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While confidential computing can protect many types of applications and data, confidential computing technology provides a secure platform for multiple parties to combine, analyze and learn from sensitive data without exposing the data or machine learning algorithms to the other party. Often referred to as multi-party computing, federated learning or privacy-preserving analytics, confidential computing can unlock the power of sensitive data by enabling collaboration while preserving privacy and compliance.
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