Microsoft, Google, and other giants create Confidential Computing Consortium to jointly protect data security

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Microsoft recently announced its inclusion in the blog to join the Confidential Computing Consortium (CCC). The organization is committed to defining and accelerating the adoption of confidential computing and will be hosted on the Linux Foundation. The founding members of the alliance also include technology companies such as Alibaba, ARM, Baidu, Google Cloud, IBM, Intel, Red Hat, Swisscom and Tencent, which provide an opportunity for the industry to gather to promote the use of confidential computing to better protect data.

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The need to establish a federated computing consortium stems from the fact that as computing moves from on-premises to public clouds and edges, the protection of data becomes more complex. Current data protection typically applies to data in static (storage) or (network) transmission states. But when data is being used, there is still a risk, which is one of the most challenging steps in data protection. Therefore, confidential computing will focus on protecting data in use and providing a fully encrypted lifecycle for sensitive data. It processes encrypted data in memory without exposing it to the rest of the system and reduces exposure to sensitive data, giving users more control and transparency.

Mark Russinovich, CTO of Microsoft Azure said:

Protecting data in use means data is provably not visible in unencrypted form during computation except to the code authorized to access it. That can mean that it’s not even accessible to public cloud service providers or edge device vendors. This capability enables new solutions where data is private all the way from the edge to the public cloud. Some of the scenarios confidential computing can unlock include:

  • Training multi-party dataset machine learning models or executing analytics on multi-party datasets, which can allow customers to collaborate to obtain more accurate models or deeper insights without giving other parties access to their data.
  • Enabling confidential query processing in database engines within secure enclaves, which removes the need to trust database operators.
  • Empowering multiple parties to leverage technologies like the Confidential Consortium Framework, which delivers confidentiality and high transaction throughput for distributed databases and ledgers.
  • Protecting sensitive data at the edge, such as proprietary machine learning models and machine learning model execution, customer information, and billing/warranty logs.

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