Source: thenewstack.io
MC2: Secure Collaborative Analytics for Machine Learning
Machine Learning (ML) has gained prominence in recent years because of its ability to be applied across scores of industries and solve complex problems effectively. Personal data is becoming more pervasive, causing privacy concerns to grow.
Secure collaborative learning enables multiple parties to build mutually robust ML models, without openly sharing sensitive data with each other.
MC2 (Multiparty Collaboration and Coopetition) enables rich analytics and machine learning on encrypted data, ensuring that data remains concealed even when it’s being processed.
Confidential computing and analytics on encrypted data will soon become a must for all industries looking to collaborate on sensitive information.
Secure collaborative learning enables multiple parties to build mutually robust ML models, without openly sharing sensitive data with each other.
MC2 (Multiparty Collaboration and Coopetition) enables rich analytics and machine learning on encrypted data, ensuring that data remains concealed even when it’s being processed.
Confidential computing and analytics on encrypted data will soon become a must for all industries looking to collaborate on sensitive information.
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