The Conference for Machine Learning Innovation

Preserve data privacy in Machine Learning with Confidential Computing

Session
Join the ML Revolution!
Register until September 15:
✓ Save up to $323
✓ 3 Day Special
✓ Team discount
Register Now
Join the ML Revolution!
Register until September 15:
✓ Save up to $323
✓ 3 Day Special
✓ Team discount
Register Now
Join the ML Revolution!
Register until August 18:
✓ Pre-conference workshops for free
✓ Save up to €503
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Register until August 18:
✓ Pre-conference workshops for free
✓ Save up to €503
✓ 10 % Team Discount
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now

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 (TEE) 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 confidential computing in a Machine Learning solution, where different financial institutes share their confidential datasets for data analysis and credit risk prediction using the ML.NET library, and still mask any sensitive information to protect the privacy of their customers, preventing any potential data leakage.

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This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore program. Take me to the program of . Hier geht es zum Programm von Munich Munich .

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