Media Alert: Intel at Open Confidential Computing Conference 2024
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Protecting systems and sensitive, confidential or regulated data, especially while in use, has never been more critical. As computing moves to span multiple environments – from on-prem to public cloud to edge – organizations need protection controls that help safeguard sensitive intellectual property (IP) and workload data wherever that data resides. Learn how
When:
Where:
Virtual Event
Registration:
Free to attend
Confidential Cloud Native Attestation – Challenges and Opportunities
Confidential computing brings with it tamper-resistant registers to measure digital ingredients, akin to what the Trusted Computing Group’s TPM 2.0 offers, such as BIOS, firmware, kernel and beyond. Clouds are varied in their infrastructure and multiple confidential computing vendors, each potentially with multiple product generations, offering confidential CPUs, GPUs and other special-purpose processing units. Further, there are at least three flavors of confidential virtual machine (CVM) use – whole confidential Kubernetes clusters, launching traditional virtual machine payloads as a CVM using KubeVirt or Virtual Kubelet, or running a confidential container, like CoCo. What should one measure, particularly with confidential clusters where workloads come and go? The trick lies in capturing invariants and keeping them separate to not have a combinatorial explosion of values to register in an attestation service as good values. Further, what is the essence that we must keep invariant to protect the workloads in the various contexts?
In this talk,
When:
Where:Virtual through conference platform
Confidential Computing in 2024 – Innovating Secure and Scalable Solutions
We are on the cusp of a transformative era. Technical readiness and market momentum will converge in 2024 to accelerate growth and adoption of confidential computing. This session, presented by Anand Pashupathy, vice president and general manager of
When:
Where:Virtual through conference platform
Tightening Side Channel Protections with
Intel® Software Guard Extensions (Intel® SGX) supports the creation of shielded enclaves within unprivileged processes. Code and data within an enclave cannot be read or modified by the operating system or hypervisor, nor by any other software. However, side-channel attacks can be challenging to comprehensively mitigate. This talk by
When:
Where:Virtual through conference platform
Asterinas: A Safe and Efficient Rust-Based OS Kernel for TEE and Beyond
In the realm of OS kernels, particularly those within virtual machine (VM) trusted execution environments (TEEs), memory safety is a paramount concern. Rust, known for its safety features, aids in developing secure kernels but is not a panacea. Firstly, Rust's unsafe features, such as pointer dereferencing and inline assembly, are necessary for low-level, error-prone tasks, often permeating the codebase. Secondly, the guest kernel in a VM TEE often processes untrusted inputs (over 1,500 instances in Linux, per
This leads us to explore how effectively a Rust-based kernel can minimize its trusted computing base (TCB) against memory safety threats, including Iago attacks. The response is Asterinas: a safe and efficient OS kernel crafted in Rust, offering Linux ABI compatibility. Asterinas introduces a groundbreaking framekernel OS architecture. This design splits the kernel into two distinct halves within the same address space: the framework and services. The framework is the sole domain allowed to utilize unsafe Rust features, providing a high-level, safe and sound API for the services, which are exclusively developed in safe Rust. The services are responsible for providing most of the OS functionalities, including enabling all peripheral devices. As the entire kernel resides in the same address space, different parts of the kernel can communicate in the most efficient way.
In this talk,
When:
Where: Virtual through conference platform
Seamless Attestation of
AI is now the most significant workload in data centers and the cloud. It’s being embedded into other workloads used for standalone deployments and distributed across hybrid clouds and the edge. Many of the demanding AI workloads require hardware acceleration with a GPU. Many AI models are considered priceless intellectual property – companies spend millions of dollars building them, and the parameters and model weights are closely guarded secrets. The datasets used to train these models are also considered highly confidential and can create a competitive advantage. As a result, data and model owners are looking for ways to protect these, not just at rest and in transit, but while in use as well.
This session presented by
When:
Where: Virtual through conference platform
The Status Quo and Potential of Confidential AI
OC3 brings back this exciting panel with industry leaders, this time to discuss confidential AI. The panelists will discuss what confidential AI is, use cases, technical challenges, regulatory incentives and limits. Panel members will also make predictions about the future of this technology. Will AI be the “killer app” for confidential computing? When will confidential computing be the standard for AI?
This panel will feature
When:
Where:Virtual through conference platform
Private Data Exchange – Leveraging Confidential Computing to Combat Human Trafficking and Modern Slavery
This session from Hope for Justice,
Organizations like Hope for
This project will enable global organizations to collaborate and share analyses to prevent human trafficking, respond to situations of exploitation and ensure victims receive the support they need, while shielding their confidential information or regulated data.
When:
Where:Virtual through conference platform
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View source version on businesswire.com: https://www.businesswire.com/news/home/20240308183003/en/
425-765-3485
jennifer.foss@intel.com
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