Current Result Document :
| 한글제목(Korean Title) |
차분 프라이버시를 이용한 딥러닝을 위한 오픈소스 툴의 종합분석 |
| 영문제목(English Title) |
Comprehensive Analysis of Open-Source Tools for Differentially Private Deep Learning |
| 저자(Author) |
우타리예바 아쎔
신진명
황보규민
이혜주
김재석
최윤호
Assem Utaliyeva
Jinmyeong Shin
Gyu-min Hwangbo
Hyeju Lee
Jaesok Kim
Yoon-Ho Choi
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| 원문수록처(Citation) |
VOL 49 NO. 01 PP. 1288 ~ 1290 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In the light of recent innovations in commercial machine learning, Differential Privacy is getting its recognition as state-of-the-art privacy preservation technique for ML and DL models. Large number of users consider differentially private open-source tools as a practical solution for deploying differential privacy into their models. In this paper, we provide comprehensive review of the three common open-source tools introduced by technical giants as IBM, Facebook, and Google. We compare these tools according to various aspects and provide recommendations for non-expert users trying to decide which tool is best for their application. |
| 키워드(Keyword) |
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| 파일첨부 |
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