![]() ![]() Machine learning services running in the TEE aggregate and analyze data. Hospitals and health institutes can collaborate by sharing their patient medical records with a centralized trusted execution environment (TEE). Disease diagnostic and drug development benefit from multiple data sources. Sometimes these organizations even want to protect data from computing infrastructure operators or engineers, security architects, business consultants, and data scientists.įor example, using machine learning for healthcare services has grown massively as we've obtained access to larger datasets and imagery of patients captured by medical devices. ![]() Public and private organizations require their data be protected from unauthorized access. The data may be personal information, financial records, medical records, private citizen data, etc. Often, the data being shared is confidential. What are the benefits of using Azure confidential computing for multi-party scenarios, enhanced customer data privacy, and blockchain networks?īusiness transactions and project collaboration require sharing information amongst multiple parties.What are some scenarios for Azure confidential computing?.The recommendations in this article serve as a starting point as you develop your application using confidential computing services and frameworks.Īfter reading this article, you'll be able to answer the following questions: This article provides an overview of several common scenarios for Azure confidential computing. The same sensitive data may contain biometric data that is used for finding and removing known images of child exploitation, preventing human trafficking, and aiding digital forensics investigations. For example, preventing access to sensitive data helps protect the digital identity of citizens from all parties involved, including the cloud provider that stores it. Confidential computing applies to various use cases for protecting data in regulated industries such as government, financial services, and healthcare institutes. ![]()
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