Preserving privacy when processing data from multiple sources with machine learning is always a challenge. Organizations may want to perform collaborative data analytics while guaranteeing the privacy of their individual datasets. Combining multiple data sources to support a better algorithmic outcome improves accuracy of prediction, but it may come at cost of confidentiality, if sensitive information is not accurately protected. Azure Confidential Computing adds new data security capabilities to the cloud and specifically to machine learning processing. By using trusted execution environments (TEEs) to protect your data while in use, with confidential computing, you can use machine learning algorithms across different organizations to better train models, without revealing the processed data. This session presents the benefits of Azure Confidential Computing in an ML scenario, where separate health institutes collaborate on data analysis and prediction using Azure Machine Learning, and still mask any sensitive information to protect the privacy of their patients.
Stefano Tempesta works at Microsoft in the Azure Confidential Computing product group to make the Cloud a more secure place for your data and apps. Additionally, as advisor to the Department of Industry, Australia, on the National Blockchain Roadmap, his current focus is on helping people gain and own their digital identity. Stefano is also technology advisor at Carbon Asset Solutions, a climate action and sustainability network with a mission to slow carbon dioxide emissions and remove excess atmospheric CO2 by using regenerative agriculture technologies.
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