HP HPE2-N69 Exam Dumps while preserving the privacy

[Amber Andrey]

Using HPE AI and Machine Learning  The goal is to analyze the data – which often is located at the edge while preserving the privacy of the data. “Largely the way that we work – not only in model development and model training, but also operation – is we tend to take all of the data collected and bring it into one core location for model training and model development,” Hotard said. “That data is in many cases collected and gathered at the edge and … in some cases moving that data from the edge to the court has implications for compliance with GDPR [the EU’s General Data Protection Regulation] and other regulations. It’s not trivial to simply move everything into one central location. The complexities of data movement, the costs of data movement when you’re dealing with federated data and particularly federated data is not only geographically federated, but it’s federated across clouds, public and  HP HPE2-N69 Exam Dumps private. The data and the data movement costs and complexity are quite high. What we’re trying to eliminate is the dependency on centralizing and consolidating data. This has ramifications within an enterprise and even across enterprises of different groups.” Rather than moving the data to a central location, with swarm learning the data analysis and model training is done where the data is located, protecting issues of data privacy and ownership. The learnings that come from the models are then shared across nodes via permissioned blockchains, which allow for access only by users with permissions. The model is refined, but the data isn’t shared. As an example, Hotard pointed to research being done by separate hospitals using personal health data whose privacy needs to be protected.

 

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