با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Privacy-enhanced multi-party deep learning
ترجمه فارسی عنوان مقاله:
یادگیری عمیق چند جانبه با حفظ حریم خصوصی
منبع:
Sciencedirect - Elsevier - Neural Networks, Accepted manuscript: doi:10:1016/j:neunet:2019:10:001
نویسنده:
Maoguo Gong, Jialun Feng, Yu Xie
چکیده انگلیسی:
In multi-party deep learning, multiple participants jointly train a deep learning model through a central server to
achieve common objectives without sharing their private data. Recently, a significant amount of progress has been
made toward the privacy issue of this emerging multi-party deep learning paradigm. In this paper, we mainly focus
on two problems in multi-party deep learning. The first problem is that most of the existing works are incapable of
defending simultaneously against the attacks of honest-but-curious participants and an honest-but-curious server
without a manager trusted by all participants. To tackle this problem, we design a privacy-enhanced multi-party
deep learning framework, which integrates differential privacy and homomorphic encryption to prevent potential
privacy leakage to other participants and a central server without requiring a manager that all participants trust.
The other problem is that existing frameworks consume high total privacy budget when applying differential privacy
for preserving privacy, which leads to a high risk of privacy leakage. In order to alleviate this problem, we propose
three strategies for dynamically allocating privacy budget at each epoch to further enhance privacy guarantees
without compromising the model utility. Moreover, it provides participants with an intuitive handle to strike a
balance between the privacy level and the training efficiency by choosing different strategies. Both analytical and
experimental evaluations demonstrate the promising performance of our proposed framework.
Keywords: privacy | multi-party deep learning | differential privacy | homomorphic encryption | privacy budget
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0