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Slashdot: VMware AMD Samsung and RISC-V Push For Confidential Computing Standards

VMware, AMD, Samsung and RISC-V Push For Confidential Computing Standards
Published on July 01, 2023 at 04:10AM
VMware has joined AMD, Samsung, and members of the RISC-V community to work on an open and cross-platform framework for the development and operation of applications using confidential computing hardware. The Register reports: Revealing the effort at the Confidential Computing Summit 2023 in San Francisco, the companies say they aim to bring about an industry transition to practical confidential computing by developing the open source Certifier Framework for Confidential Computing project. Among other goals, the project aims to standardize on a set of platform-independent developer APIs that can be used to develop or adapt application code to run in a confidential computing environment, with a Certifier Service overseeing them in operation. VMware claims to have researched, developed and open sourced the Certifier Framework, but with AMD on board, plus Samsung (which develops its own smartphone chips), the group has the x86 and Arm worlds covered. Also on board is the Keystone project, which is developing an enclave framework to support confidential computing on RISC-V processors. Confidential computing is designed to protect applications and their data from theft or tampering by protecting them inside a secure enclave, or trusted execution environment (TEE). This uses hardware-based security mechanisms to prevent access from everything outside the enclave, including the host operating system and any other application code. Such security protections are likely to be increasingly important in the context of applications running in multi-cloud environments, VMware reckons. Another scenario for confidential computing put forward by Microsoft, which believes confidential computing will become the norm -- is multi-party computation and analytics. This sees several users each contribute their own private data to an enclave, where it can be analyzed securely to produce results much richer than each would have got purely from their own data set. This is described as an emerging class of machine learning and "data economy" workloads that are based on sensitive data and models aggregated from multiple sources, which will be enabled by confidential computing. However, VMware points out that like many useful hardware features, it will not be widely adopted until it becomes easier to develop applications in the new paradigm.

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