直播回放 Generative Adversarial Networks: Privacy and Security Applications 生成对抗网络:隐私和安全应用
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1. Topic: Generative Adversarial Networks: Privacy and Security Applications
    演讲题目:生成对抗网络:隐私和安全应用
2. Language: English
    语言:英文
3. Speaker: Dr. Zhipeng Cai is currently an Associate Professor at Department of Computer Science, Georgia State University, USA. He received his PhD and M.S. degrees in the Department of Computing Science at University of Alberta. Dr. Cai's research areas focus on Internet of Things, Machine Learning, Cyber-Security, Privacy, Networking and Big data. Dr. Cai is the recipient of an NSF CAREER Award. Dr. Cai has been serving as an Associate Editor-in-Chief for Elsevier High-Confidence Computing Journal (HCC), and an Associate Editor for more than 10 international journals, including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Wireless Communication, IEEE Transactions on Vehicular Technology (TVT). Dr. Cai has published more than 100 papers in prestigious journals with more than 60 papers published in IEEE/ACM Transactions/Journals.
报告人:蔡志鹏博士现任美国佐治亚州立大学计算机科学系副教授,在阿尔伯塔大学计算机科学系获得博士和硕士学位。蔡博士的研究领域集中在物联网、机器学习、网络安全、隐私、网络和大数据。蔡博士曾获美国国家科学基金会职业奖,并担任爱思唯尔High-Confidence Computing Journal(HCC)副主编,以及10多家国际期刊的副主编,包括IEEE出版的Transactions on Knowledge and Data Engineering (TKDE)、Transactions on Wireless Communication及Transactions on Vehicular Technology (TVT)。蔡博士在知名期刊上发表了100多篇论文,其中在IEEE/ACM期刊及会刊上发表了超过60篇论文。
4. Abstract: Generative Adversarial Networks (GANs) are widely applied to estimate a density function over an unknown data-generating distribution. A variety of GAN models have been proposed to improve the performance of data publication, data management, knowledge discovery, information fusion, etc. Besides benefit, GAN also bring unique challenges to people, among which privacy issues are extremely urgent yet intractable concerns to be extensively investigated. In this talk, we will introduce three novel GAN models in cybersecurity domain, including Seed Free Graph De-anonymization, Privacy Graph Embedding Data Publication and Generative Adversarial Networks for Auto-Driving Vehicles. The results of extensive real-data experiments validate the superiority of our proposed models.
摘要:生成对抗网络(GAN)广泛应用于未知数据生成分布上的密度函数估计。为了提高数据发布、数据管理、知识发现、信息融合等方面的性能,人们提出了各种各样的GAN模型。除了带来了好处之外,GAN也给人们带来了独特的挑战,其中隐私问题是一个迫在眉睫又棘手的问题,需要广泛的研究支持。本次报告将为大家介绍三种新型的网络安全领域的GAN模型,包括无种子图去匿名化、隐私图嵌入数据发布和自动驾驶车辆的生成对抗网络。大量的实际数据实验结果验证了本文模型的优越性。

教授 阿尔伯塔大学 综合评分 5.0
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