Privacy-Preserving Deep Learningdelivery.acm.org/10.1145/2820000/2813687/p1310-shokri.pdf?ip=121.195.114.53&id=2813687&acc=ACTIVE%20SERVICE&key=33E289E220520BFB.583BB86AD7417D9A.4D4702B0C3E38B35.4D4702B0C3E38B35&__acm__=1560080619_a3df4bdcc636689b11a7b5f73d21a292 创新点 模型训练过程中选...
1、Privacy-Preserving Deep LearningCCS15,October 1216, 2015, Denver, Colorado, USA Reza Shokri Vitaly Shmatikov The University of Texas at Austin Cornell Tech 报告人:许元馨 2015/12/31动机私人的数据共享在许多领域是不被法律或法规所允许的,尤其是那些与医学相关的领域。因此,生物医学、临床研究人员只能...
【论文笔记】End-to-end privacy preserving deep learning on multi-institutional medical imaging 一个樱桃喔 4 人赞同了该文章 针对什么问题? 对数据驱动解决方案的日益增长的需求可能会增加与健康相关的数据收集,来自医疗成像数据集、临床记录和医院患者数据。因此,需要创新的解决方案来协调数据并保护隐私。 使用什么...
Privacy-Preserving Deep Learning Privacy-PreservingDeepLearning CCS’15,October12–16,2015,Denver,Colorado,USA RezaShokriTheUniversityofTexasatAustinshokri@cs.utexas.edu VitalyShmatikovCornellTechshmat@cs.cornell.edu 报告人：许元馨 2015/12/31 动机 私人的数据共享在许多领域是不被法律或法规所允许的，尤其是...
Privacy-Preserving Deep Learning 1.introduction 在本文中,我们设计、实现和评估了一个实用的系统,该系统使多方能够针对给定目标共同学习一个精确的神经网络模型,而无需共享其输入数据集。我们利用了一个事实,即现代深度学习中使用的优化算法,即基于随机梯度下降的优化算法,可以并行化并异步执行。我们的系统允许参与者...
4 Our system:privacy-preserving deep learning without accuracy decline 我们的系统如图4所示,由一个公共云服务器和N个(e.g.=10x)学习参与者组成。 Learning Participants.参与者共同设置公钥pk和密钥sk,以实现加法同态加密方案。密钥sk对云服务器保密,但所有学习参与者都知道。每个参与者将建立一个彼此不同的TLS...
对于如何进行梯度的交换，本文提供两种方法：其一，按梯度绝对值大小排序，选取前k大梯度上传；其二，筛选所有参数梯度绝对值大于阈值的梯度，并从中随机选取一部分上传。此步骤后，其他用户可下载上传的梯度，用于更新本地参数。用户上传下载梯度的顺序可选择Round Robin、随机顺序或异步方式，以适应不同场景...
We also present a case study on privacy-preserving machine learning techniques. Herein, we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance. We explain the implementation details of a case study of a secure prediction service using the convolutional ...
Moreover, other researchers questioned the security of federated learning [57]. There are computationally expensive solutions proposed to address this problem. Phong et al. [52] presented a privacy-preserving deep learning system in which different learning participants perform deep learning over a ...
Here we present PriMIA (Privacy-preserving Medical Image Analysis), a free, open-source software framework for differentially private, securely aggregated federated learning and encrypted inference on medical imaging data. We test PriMIA using a real-life case study in which an expert-level deep ...