Enterprise model education: A privacy -preserving strategy
Keywords:
privacy-preserving model training, enterprise data security, differential privacy, federated learning,Abstract
Organizations increasingly rely on ML models in the modern data-centric environment in order to get insights & guide decisions. Particularly in cases involving sensitive data, the growing awareness for data privacy presents significant challenges for training these models. This work explores new approaches to build machine learning models stressing privacy without sacrificing accuracy or speed. While protecting private data by means of federated learning, differential privacy & the homomorphic encryption, organizations may teach models on distributed data sources. This approach follows legislative requirements on data security & reduces the risks connected to data breaches. The focus is on creating a structure that lets companies utilize their data while preserving personal privacy. This article emphasizes the importance of harmonizing technological development with ethical concerns so that data privacy and business success live peacefully in the future.
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