. https://www.ericsson.com/en/blog/2020/2/training-a-machine-learning-model

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3 WAYS TO TRAIN A SECURE MACHINE LEARNING MODEL
Training a machine learning model requires a large quantity of high-quality data. One way to acquire this is to combine data from multiple organizations. However, data owners are often unwilling or unable to share their data openly due to privacy concerns and regulatory hurdles. For example, enterprises naturally want to protect the privacy of their customer data or prevent sensitive data about their operations from being leaked to their competitors. Secure collaborative learning enables multiple parties to build a mutual, robust machine learning model without openly sharing their data with each other  addressing concerns over data privacy and access rights. Banks can collaboratively train models to detect money laundering while keeping their individual transaction data private. Healthcare institutions can privately pool their patient data so they can collaborate on medical studies. Mobile network operators can predict fluctuations in call rates by collectively analyzing their traffic data. The possibilities are vast and promising.In this blog post, we introduce a new platform for secure collaborative learning that’s currently under development at UC Berkeley’s RISELab and share Ericsson’s plans to apply the platform to telecom use cases through a new research partnership. READ MORE