报告名称：密码学及信息安全学术交流报告会

报告时间：11月27日9:00

报告地点：腾讯会议直播（ID:486 366 056）

主办单位：数学与统计集团

报告1：Distributed Learning in e-health

讲座人介绍：

张宽博士是美国内布拉斯加大学林肯分校电气与计算机工程系助理教授。2016年，他获得了加拿大滑铁卢大学电气与计算机工程学博士学位。2016-2017年，他在滑铁卢大学电气和计算机工程系任博士后。他在期刊和会议上发表了100多篇论文。他的研究兴趣包括网络安全、大数据和云/边缘计算。张博士于2017年获得了IEEE可扩展计算技术委员会（TCSC）卓越奖的杰出博士论文奖。他曾获得IEEE WCNC 2013、Securecomm2016和ICC 2020的最佳论文奖。并担任IEEE Transactions on Wireless Communication, IEEE Communications Surveys & Tutorials, IEEE Internet-of-Things Journal, Peer-to-Peer Network and Applications等期刊的副主编。

讲座内容：

E-health allows smart devices and medical institutions to collaboratively analyze patients’ data, which is trained by Artificial Intelligence (AI) to help doctors make diagnosis. By allowing multiple devices to train models collaboratively, federated learning is a promising solution to address the communication and privacy issues in e-health. However, applying federated learning in e-health faces many challenges. For example, medical data is both horizontally and vertically partitioned. Since single Horizontal Federated Learning (HFL) or Vertical Federated Learning (VFL) techniques cannot deal with both types of data partitioning, directly applying them may consume excessive communication costs due to transmitting a part of raw data when requiring high modeling accuracy. In this work, we present a thorough study on an effective integration of HFL and VFL, to achieve communication efficiency and overcome the above limitations when data is both horizontally and vertically partitioned. Specifically, we propose a hybrid federated learning framework with one intermediate result exchange and two aggregation phases. Based on this framework, we develop a Hybrid Stochastic Gradient Descent (HSGD) algorithm to train models. Then, we theoretically analyze the convergence upper bound of the proposed algorithm. Using the convergence results, we design adaptive strategies to adjust the training parameters and shrink the size of transmitted data. Experimental results validate that the proposed HSGD algorithm can achieve the desired accuracy while reducing the communication cost, and they also verify the effectiveness of the adaptive strategies.

报告2：区块链中的隐私保护

讲座人介绍：

杨波，陕西师范大学计算机科学集团二级教授、博士生导师，陕西省省级人才特聘教授，中国密码学会理事，中国密码学会密码算法专业委员会委员，《密码学报》编委。已主持国家重点研发项目、国家自然科学基金、国家密码发展基金等项目20余项。发表学术论文300余篇，其中被三大检索收录100余篇，出版学术著作及教材6部。

讲座内容：

报告以基于区块链密码货币的角度审视区块链中的隐私保护，以密码货币的不可重复支付、找零钱问题、支付的不可跟踪性和匿名性为目标，以零币(zerocoin)、零钞(zerocash)为例，介绍区块链中隐私保护的方法。