Announcing: Microsoft moves $25 Billion in credit card transactions to Azure confidential computing
Microsoft is proud to showcase that customers in the financial sector can rely on public Azure to add confidentiality to provide secure and compliant payment solutions that meet or exceed industry standards. Microsoft is committed to hosting 100% of our payment services on Azure, just as we would expect our customers to do. Microsoft’s Commerce Financial Services (CFS) has completed a critical milestone by deploying a level 1 Payment Card Industry Data Security Standard (PCI-DSS) compliant credit card processing and vaulting solution, moving $25 Billion in annual credit card transactions to the public Azure cloud.Confidential VM node pool with AMD SEV-SNP protection available on AKS in public preview
AKS node pools now support the generally available confidential VM sizes (DCav5/ECav5). Confidential VMs with AMD SEV-SNP support bring a new set of security features to protect date-in-use with full VM memory encryption. This enables confidential VM node pools to target the migration of highly sensitive container workloads to AKS without any code refactoring while benefiting from the full AKS feature support.Preview of multiparty analytics with Azure Confidential Clean Rooms
Today, we are excited to announce the preview of multiparty analytics feature of Azure Confidential Clean Rooms, a fully managed service that allows customers and their partners to securely analyze privacy-sensitive datasets from multiple parties. It uses confidential compute enabled Apache Spark-based big-data analytics (Spark SQL) which helps protect their raw data from other collaborators and from the Azure operator by performing computations in a Trusted Execution Environment (TEE). Privacy-sensitive datasets include personally identifiable information (PII), protected health information (PHI) and cryptographic secrets. Organizations across industries are increasingly looking to supplement their data with data from business partners, to build a complete view of their business. For example, brands, publishers, and their partners need to collaborate using datasets containing Intellectual Property (IP) to improve the relevance of their campaigns. Confidential data clean rooms help solve this challenge by enabling organizations to share and analyze granular datasets in a secure environment that helps prevent raw data exfiltration—protecting intellectual property, preserving customer privacy, and addressing concerns around regulatory compliance. You can sign up for the preview here Key Features Fully Managed: Azure takes care of the infrastructure provisioning and scaling with no user intervention. This significantly reduces your onboarding effort allowing you to focus on the queries and insights, not on infra management. Confidential Spark SQL: Spark SQL allows you to query large datasets and run complex queries in a distributed computing environment. In the confidential computing enabled version, the Spark driver and executors are fully attested policy-governed enclaves running as virtual nodes on confidential Azure Container Instances (ACI) which helps prevent exfiltration of collaborators’ data during query execution. Governance: Helps manage membership to cleanrooms, enables and verifies approval for queries from relevant collaborators before executing them and verifies consent to access sensitive collaborator data. It also helps generate tamper-resistant audit trails containing salient clean room events. This is made possible with the help of an implementation of the Confidential Consortium Framework (CCF). Telemetry: Throughout every clean-room run, detailed logs are streamed out in real time to monitor performance, troubleshoot issues, and keep the analytics healthy — all without ever exposing the collaborators’ data at any time. Verifiable trust: Cryptographic remote attestation viz. full attestation based on confidential hardware reports allows independent verification of the TEE along with along with all components that are part of it, without just trusting the cloud provider, before sensitive data and decryption keys are made available to the TEE Open-source containers: All Microsoft provided cleanroom containers and sidecars are open-sourced here and can be verified for provenance and integrity guarantees using GitHub artifact attestation Use Cases Multi-party confidential big-data analytics unlocks value in scenarios where data sensitivity, regulatory pressure, or competitive concerns previously blocked collaboration. These are some early scenarios that can benefit from this. Media & Advertising Collaboration of advertiser CRM data with publisher data for audience targeting and segment activation. Collaboration of audience data with measurement partners for measurement and attribution. Banking & Finance Collaboration between banks and insurance firms to upsell relevant products to existing bank customers without sharing raw data from either side Collaboration with retailers to generate customized offers for bank customers, without exposing either party’s underlying data. Government & Public Sector Secure collaboration of data across government departments to deliver better citizen welfare outcomes. Secure collaboration between government and private enterprises on shared-interest workloads such as traffic monitoring and weather systems. Healthcare Enable healthcare firms — including biopharma organizations — to combine their data with third-party institutions to accelerate clinical development, like identifying eligible participants for a clinical trial, without exposing underlying patient data. Combine patient datasets across hospitals to study disease patterns or outcomes without exposing sensitive protected health information. "A higher standard for protecting user privacy and trust, the phase-out of third-party cookies, and global regulations demand more sophisticated data collaboration tools to support advertising marketplaces. Azure Confidential Cleanrooms (ACCR) provides a secure, feature-rich, and flexible foundation to implement privacy-preserving functions and enable insights without sharing privacy-sensitive data outside of organization boundaries. Built on the Azure Confidential Compute (ACC) platform and offering cohesion with Azure's diverse set of services, ACCR offers the attestation, audit, fine-grained access control, and verifiable trust tools required for secure and privacy-safe data collaboration in today's world." — Andrei Mackenzie, Engineering Manager, Microsoft AI "Azure Confidential Clean Rooms enabled our team to evaluate how clean room capabilities can support secure, governed data collaboration at scale. Through the Proof-of-Concept (PoC), we explored how privacy-preserving workflows, trusted access controls, and scalable compute can create a stronger foundation for responsibly leveraging first-party data. This helps reduce operational friction while supporting business growth, improving customer engagement, and enabling more relevant customer experiences." — Nic Dregne, Director, Microsoft AdTech Engineering Beyond Spark SQL Realizing other multi-party scenarios like custom analytics, ML training and inferencing on Azure Confidential Clean Rooms is in our roadmap. If you have such a scenario to be realized, you can fill in and submit the preview signup form with the details of your scenario and we’ll get back to you. Learn More · Signup for the preview of Azure Confidential Clean Rooms for Analytics · Confidential Consortium Framework (CCF) · Virtual Nodes on Azure Container Instances
BigDL Privacy Preserving Machine Learning with Occlum OSS on Azure Confidential Computing
This blog introduces you to a confidential computing solution for Privacy-Preserving Machine Learning (PPML) made available by Open-Source Software Occlum Library OS for Intel SGX and BigDL on the Azure cloud. This blog demonstrates the solution using a sample analytics application built for the NYTaxi dataset. This sample application leverages Azure Confidential Computing (ACC) components such as SGX Nodes for Azure Kubernetes Service (AKS), Microsoft Azure Attestation, Azure Key Vault (AKV), etc, as well as Occlum LibOS and BigDL PPML.
