
The author analyzes the problems with probabilistic truncation protocols in PPML, focusing on accuracy and efficiency, proposing solutions to address these issues.


coremsg

addressing-challenges-in-probabilistic-truncation-for-enhanced-privacy-preserving-machine-learning


Addressing Challenges in Probabilistic Truncation for Enhanced Privacy-Preserving Machine Learning


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Existing truncation protocols suffer from errors, particularly e1, impacting accuracy and efficiency in PPML tasks.


bicoptor-2-0-addressing-challenges-in-probabilistic-truncation-for-enhanced-privacy-preserving-machine-learning


Bicoptor 2.0: Addressing Challenges in Probabilistic Truncation for Enhanced Privacy-Preserving Machine Learning



해당 논문은 기존의 개인정보 보호 기계 학습에서의 확률적 절단 프로토콜의 문제점을 분석하고 해결책을 제안하는 데 초점을 맞추고 있습니다.
