Neustar’s white paper introduces privatized machine learning (PML) as a way to unlock marketing insights while preserving data privacy. PML enables brands and data owners to collaborate without sharing raw data, using federated learning and privacy-preserving techniques like differential privacy and homomorphic encryption. This approach maintains compliance with regulations while improving model performance. It supports applications like attribution, media optimization, and audience segmentation. By enabling secure, scalable access to sensitive data, PML bridges the gap between marketing effectiveness and consumer privacy protection.

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