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This document presents a multi-party privacy-preserving logistic regression framework that addresses both privacy concerns and poor data quality in IoT data contributions. The authors propose a new metric, gradient similarity (Gsim), to filter out poor quality data while employing homomorphic encryption for privacy preservation. Experimental evaluations demonstrate the framework

Author

SimoMouiti

Language

EN