Confidential Data Clean Rooms – The evolution of sensitive data collaboration
Secure data collaboration between multiple parties has the potential to revolutionize societies, businesses and industries for the better. Collaborating on sensitive data assets facilitates innovation to unlock new value for organizations.
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.Frictionless Collaborative Analytics and AI/ML on Confidential Data
Secure enclaves protect data from attack and unauthorized access, but confidential computing presents significant challenges and obstacles to performing analytics and machine learning at scale across teams and organizational boundaries. In this article, we'll explore the Opaque platform and describe how it can enable multiple parties to easily collaborate and analyze shared data while keeping it fully confidential.Preview of Azure Confidential Clean Rooms for secure multiparty data collaboration
Today, we are excited to announce the preview of Azure Confidential Clean Rooms, a cutting-edge solution designed for organizations that require secure multi-party data collaboration. With Confidential Clean Rooms, you can share privacy sensitive data such as personally identifiable information (PII), protected health information (PHI) and cryptographic secrets confidently, thanks to robust trust guarantees that help ensure that your data remains protected throughout its lifecycle from other collaborators and from Azure operators. This secure data sharing is powered by confidential computing, which helps protect data in-use by performing computations in hardware-based, attested Trusted Execution Environments (TEEs). These TEEs help prevent unauthorized access or modification of application code and data during use. Organizations across industries need to perform multi-party data collaboration with business partners, outside organizations, and even within company silos to improve business outcomes and bolster innovation. Confidential Clean Rooms help derive true value from such collaborations by enabling granular and private data to be shared while providing safeguards on data exfiltration hence protecting the intellectual property of the organization and the privacy of its customers and addressing concerns around regulatory compliance. Whether you’re a data scientist looking to securely fine-tune your ML model with sensitive data from other organizations, or a data analyst wanting to perform secure analytics on joint data with your partner organizations, Confidential Clean Rooms will help you achieve the desired results. You can sign up for the preview here Key Features Secure Collaboration and Governance: Allows collaborators to create tamper-resistant contracts that contain the constraints which will be enforced by the clean room. Governance verifies validity of those constraints before allowing data to be released into clean rooms and helps generate tamper-resistant audit trails. This is made possible with the help of an implementation of the Confidential Consortium Framework CCF). Enhanced Data Privacy: Provides a sandboxed execution environment which allows only authorized workloads to execute and prevents any unauthorized network or IO operations from within the clean room. This helps keep your data secure throughout the workload execution. This is possible with the help of deploying clean rooms in confidential containers on Azure Container Instances (ACI) which provides container group level integrity with runtime enforcement of the same. Verifiable trust at each step with the help of cryptographic remote attestation forms the cornerstone of Confidential Clean Rooms. Salient Use Cases Azure Confidential Clean Rooms caters to use cases spanning multiple industries. Healthcare: For fine-tuning and inferencing with predictive healthcare machine-learning (ML) models and for joint data analysis for advancing pharmaceutical research. This can help protect the privacy of patients and intellectual property of organizations while demonstrating regulatory compliance. Finance: For financial fraud detection through analysis of combined data across banks and other financial institutions and for providing personalized offers to customers through secure analysis of transaction data and purchase data in retail outlets Media and Advertising: For improving marketing campaign effectiveness by combining data across advertisers, ad-techs, publishers and measurement firms for audience targeting and attribution and measurement Retail: For enhanced personalized marketing and improved inventory and supply chain management Government and Public Sector Organizations: For analysis of high security data across multiple government and public sector organizations to streamline benefits for citizens Customer Testimonials We are already partnering with several organizations to accelerate their secure multi-party collaboration journey with confidential clean rooms. Confidential computing in healthcare allows secure data processing within isolated environments, called 'clean rooms', protecting sensitive patient data during AI model development, validation and deployment. Apollo Hospitals uses Azure Confidential Clean Rooms to enhance data privacy, encrypt data, and securely train AI models. The benefits include secure collaboration, anonymized patient privacy, intellectual property protection, and enhanced cybersecurity. Apollo’s pilot with Confidential Clean Rooms showed promising results, and future efforts aim to scale secure AI solutions, ensuring patient safety, privacy, and compliance as the healthcare industry advances technologically. - Dr. Sujoy Kar, Chief Medical Information Officer and Vice President, Apollo Hospitals Azure Confidential Clean Rooms is a game changer to make collaborations on sensitive data both seamless and secure. When combined with Sarus, any data processing job is automatically analyzed using the most advanced privacy technology. Once validated, they are processed securely in Confidential Clean Rooms protecting both the privacy of data and the confidentiality of the analysis itself. This eliminates administrative overheads and makes it very easy to build advanced data processing pipelines. With our partner EY, we're already leveraging it to help international banks improve AML practices without compromising privacy. - Maxime Agostini, CEO & Cofounder of Sarus Read here to learn more about how Sarus is using Confidential Clean Rooms. As co-leaders on this Data Consortium Pilot, we are thrilled to be working with industry partners, Sarus and Microsoft, to drive this initiative forward. By combining Sarus’ privacy preserving technologies and Microsoft’s Azure Confidential Clean Rooms, not only does this project push the edge of technology innovation, but it strives to address a pivotal issue that affects us as Canadians. Through this work, we aim to help financial services organizations and regulators navigate the complexities of private and personal data sharing, without compromising the integrity of the data, and adhering to all relevant privacy regulations. For the purposes of this pilot, we are focusing our efforts on how this technology can play a pivotal role in helping better detect cases of human trafficking, however, we recognize that it can be used to help organizations for multiple other use cases, and cross industries, including health care and government & public sector. - Jessica Hansen, Privacy Partner EY Canada, and Dana Ohab, AI & Data Partner EY Canada Retrieval-Augmented Generation (RAG) applications accessing Large Language Models (LLMs) are common in private AI workflows, but managing secure access to sensitive data can be complex. SafeLiShare’s integration of its LLM Secure Data Proxy (SDP) with Azure Confidential Clean Rooms (ACCR) simplifies access control and token management. The joint solution helps ensure runtime security through advanced Public Key Infrastructure (PKI) and centralized policy management in Trusted Execution Environments (TEEs), enforcing strict access policies and admission controls to guarantee authorized access to sensitive data. This integration establishes trust bindings between the Identity Provider (IDP), applications, and data, safeguarding each layer without compromise. It also enables secure creation, sharing, and management of applications and data assets, ensuring compliance in high-performance AI environments. - Cynthia Hsieh, VP of Marketing, SafeLiShare Read here to learn more about how SafeLiShare is using Confidential Clean Rooms. Learn More Signup for the preview of Azure Confidential Clean Rooms Confidential Consortium Framework (CCF) Confidential containers on Azure Container Instances (ACI)Confidential agentic AI on Azure helps ServiceNow respond to sales commission inquiries in seconds
Introduction AI is transforming how businesses operate and innovate, unlocking opportunities across industries to pioneer new business models, solve previously intractable challenges, and create breakthrough experiences. ServiceNow is at the forefront, deploying powerful, confidential AI agents leveraging confidential computing. Their sales commission help desk faced mounting challenges, supporting their sales force. With thousands of commission inquiries annually requiring access to sales compensation plan information, the help desk needed a solution to accelerate response times while maintaining strict data privacy and security standards. Applications ServiceNow Digital Technology leverages cutting-edge AI to streamline business operations, increase operational efficacy, and enhance employee experiences. The sales commission help desk handles inquiries ranging from policy questions to payout explanations, requiring aggregation and analysis of sensitive data from multiple systems. The manual process of responding to these inquiries—which involves gathering, anonymizing, and analyzing sensitive employee data, sales quotas, and commission structures—created bottlenecks, with resolution times stretching to days for the most complex cases. Use Cases To address ongoing commission management challenges, ServiceNow’s Digital Technology team partnered with Opaque Systems and Azure confidential computing team to design, build, and implement Confidential Agents that enable secure, autonomous AI systems with cryptographic privacy guarantees and auditability. The solution provides their help desk team with instantaneous access to encrypted personal commission data across multiple systems, while AI agents automatically analyze requests and generate custom responses. By integrating securely with various data sources, the system maintains strict privacy controls and compliance while delivering rapid, trusted insights. Every action taken by the AI agents is cryptographically verified, creating an immutable record of data access and usage. This generates detailed audit trails that meet compliance and strengthen governance protocols. Through hardware-based encryption on Azure NCCads H100 v5 confidential virtual machines augmented by NVIDIA H100 Tensor Core GPUs for accelerated computing running on their Microsoft Azure subscription service, the services built by ServiceNow can now harness the full power of AI technology without compromising on capabilities. Opaque’s Confidential AI Platform unlocks new performance potential of AI models that demand high-performing computational resources for all the commission requests, while maintaining robust protection of compensation data, setting a new standard for secure, efficient commission management. Accelerate, Reclaim, and Save Opaque's Confidential AI Agents architecture was uniquely built with NVIDIA H100s to help ServiceNow’s transformation. Once AI agents are connected to sensitive data, every aspect of agent operation maintains verifiable privacy and security, including real-time attestation that verifies agent authenticity and integrity, comprehensive audit trails of all agent actions and data interactions, cryptographic enforcement of data access and usage policies, and protection of valuable agent models and intellectual property. This combination of autonomous capability and verifiable privacy and security makes it ideal for ServiceNow to leverage for sensitive sales commission data while maintaining the highest standards of privacy and trust. The implementation of Opaque's Confidential AI Agents delivered strong results across ServiceNow's sales operations. Average response times decreased from 4 days to just 8 seconds, dramatically improving service delivery. Sellers can find quick summaries of Sales Success Center material and links to learn more. They also reported a 74% accuracy rate of agent responses, demonstrating high relevancy. Beyond operational improvements, ServiceNow improved operating costs while simultaneously strengthening their security posture through confidential computing. Most importantly, this has freed up the help desk team to focus on more strategic, high-value work while delivering faster, more accurate support to the sales force, creating a virtuous cycle of improved efficiency and satisfaction. Learn more Azure confidential VMs with NVIDIA H100 Tensor Core GPUs Azure confidential GPU Options Opaque’s Confidential AI Platform NVIDIA H100 Tensor Core GPUsPreview 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 InstancesImprove Campaign Reach and Measurement with Azure Confidential Clean Rooms
Why campaign performance now depends on trusted data collaboration For many years, third-party cookies helped advertisers, publishers, and measurement partners recognize users across parts of the open web. That model continues to evolve as browsers and mobile platforms introduce stronger privacy protections and limit access to certain cross-site and device-level identifiers. As a result, some advertisers and publishers are placing greater emphasis on the first-party data they collect directly to support audience activation and campaign measurement. Advertising partnerships may involve identifiers and behavioral data covered by privacy, data-protection, or industry rules. Data that has been altered to reduce direct identification—such as hashed email addresses or advertising identifiers—may still be linked to individuals and should not automatically be considered anonymous. Depending on the law and context, it may be treated as personally identifiable information (PII) or personal data under the European Union’s General Data Protection Regulation (GDPR). Organizations must have an appropriate legal basis and a clear purpose for using such data. The European Union’s Digital Markets Act (DMA) also affects how designated digital platforms combine certain personal data across services. In the United States, health information held by organizations covered by the Health Insurance Portability and Accountability Act (HIPAA) may be subject to additional restrictions, including for some marketing uses. The requirements for each collaboration depend on the parties, data, purpose, consent or authorization, and jurisdiction. Together, these changes create a practical challenge: campaign analysis increasingly depends on combining detailed records held by different organizations, but privacy, security, and governance obligations make unrestricted exchange of customer-level data difficult. Advertisers and publishers therefore need a controlled way to match audiences and measure outcomes using only authorized data—without exposing or freely sharing the underlying records. Azure Confidential Clean Rooms is designed to address this challenge. It allows each party’s detailed data to be used for an agreed analysis while helping protect raw data from access by other participants. This provides a controlled, verifiable environment for audience matching and campaign measurement without unrestricted file sharing. What campaign leaders should look for: the potential to broaden addressable audiences, strengthen evidence of campaign lift, reduce operational risk, and establish a repeatable model for trusted collaboration across media and measurement partners. Campaign decisions with secure multiparty analytics Azure Confidential Clean Rooms is a fully managed service that enables advertisers, publishers, agencies, and measurement partners to analyze sensitive datasets together while helping protect each participant’s raw data. The current preview supports analytics through Spark SQL queries agreed by the participants. These queries run in a hardware-protected environment called a Trusted Execution Environment (TEE), helping protect participants’ raw data from other collaborators and the Azure operator throughout its lifecycle. For a deeper explanation of the technology and architecture, see our earlier blog, Preview of multiparty analytics with Azure Confidential Clean Rooms. The following scenarios show how this model supports specific campaign decisions: Audience activation — Which high-value customers might this media partner reach? The advertiser contributes first-party customer data, such as securely transformed contact details, postal addresses, loyalty IDs, and purchase history. The publisher contributes its audience-matching data, on-site behavior, and consented interest or demographic segments. Azure Confidential Clean Rooms can compare identifiers that the participants have agreed to use for matching and release a reachable audience segment without exposing the underlying records. Campaign leaders can use the result to estimate potential reach and direct investment toward more relevant audiences. Identity enrichment — Can we improve match quality while protecting partner data? An agency or identity partner can contribute a third dataset to connect approved identifiers across email, mobile advertising IDs, devices, or households and add consented demographic or business attributes. Because the matching happens inside the confidential clean room, participants may improve match rates without exposing the identity partner’s underlying data or copying another party’s identifiers. Stronger matching can help expand reach and reduce media waste caused by fragmented customer identities. Measurement and attribution — Did the campaign contribute to additional business outcomes? The publisher contributes ad-exposure records, while the advertiser contributes outcomes such as purchases, order values, subscriptions, or app installs. Comparing these approved datasets can help measure unique reach and frequency, results across publishers, and the difference between groups that were and were not exposed to the campaign. For example, a retailer and a streaming publisher can compare exposure data with purchase outcomes to estimate campaign lift without revealing who saw a specific ad or made a purchase. This can give campaign leaders stronger evidence for campaign optimization and future budget allocation. These scenarios can help campaign leaders assess whether secure data collaboration may improve audience reach, matching quality, or confidence in campaign measurement. Why campaign leaders should consider Azure Confidential Clean Rooms The collaboration model is designed to give each participant greater control over how its data is accessed, used, and shared. Four capabilities are central to building trust among advertisers, publishers, agencies, and measurement partners: No centralized data pooling — Collaborators can keep source datasets in their own storage instead of copying them into a shared, permanent repository. The confidential environment verifies that expected code is running before it securely retrieves authorized data for temporary processing. This can reduce unnecessary data movement and exposure while supporting an approved analysis across partners. Controlled release of approved results — Before analysis begins, collaborators can agree which data may be used, approve the analysis, and specify who may receive the results. Safeguards can also prevent results for very small groups from being released, helping reduce the risk of identifying an individual. Verifiable governance and tamper-resistant audit trails — Participants can verify key properties of the environment instead of relying only on the clean-room provider’s claims. Open-source components and hardware-backed verification help participants confirm that the expected Microsoft-provided code is running. Tamper-resistant audit trails generated in the confidential environment can support compliance reviews. Data protection throughout the process — Each party can encrypt its data before it leaves its environment. Participants allow access to encryption keys only after the confidential environment verifies that expected code is running. Try Azure Confidential Clean Rooms Consider a campaign where better partner data could help improve audience reach, matching quality, or measurement confidence. Sign up to participate in a preview of multiparty analytics with Azure Confidential Clean Rooms. The Microsoft team will contact you to discuss the campaign scenario, participating datasets, and governance requirements. Learn more Sign-up: Preview of multiparty analytics with Azure Confidential Clean Rooms Preview blog: Multiparty analytics with Azure Confidential Clean Rooms Azure documentation: Confidential Clean Rooms
