学术报告|Trustworthy Machine Learning: Security,Privacy,and Fairness

机器学习技术作为当前火热的研究领域，已经投入到我们生活中的种种应用，与我们的生活息息相关，例如购物平台的推荐系统、浏览器的搜索引擎、手机支付使用的人脸识别等等。这些应用利用机器学习技术提高其服务水平，使得我们的生活更加便利、大大提高了人类的工作生产效率。但机器学习发挥强大作用的原因之一是其利用的数据，当数据来源于真实世界时，这就带来了数据隐私泄露的安全隐患。机器学习的安全与隐私保护也已成为研究热点，本次报告是由浙江大学网络空间安全学院组织的有关机器学习中安全、隐私、公平问题的学术报告。
原文来自公众号：浙大网安

浙江大学网络空间安全学院学术报告

报告时间2022年11月8日（周二）10:00会议平台Zoom

链接：https://auburn.zoom.us/j/3348446330

Trustworthy Machine Learning: Security, Privacy, and Fairness

报告摘要：

With continued advances in science and technology, digital data have grown at an astonishing rate in various domains and forms, such as business, geography, health, multimedia, network, text, and web data. Machine learning, a powerful tool for automatically extracting, managing, inferencing, and transferring knowledge, has been proven to be extremely useful in understanding the intrinsic nature of real-world big data. Despite achieving remarkable performance, machine learning models, especially deep learning models, suffer from severe security and privacy threats caused by malicious users, hackers, and spies or undermine fairness by inadvertently discriminating against specific demographic groups. There is an immediate and crucial need for theoretical and practical techniques to identify the vulnerability of machine learning models and explore the defense mechanism to ensure they are trustworthy.

In this talk, I will introduce problems, challenges, and solutions for characterizing and understanding vulnerability, privacy risks, and unfairness of machine learning models in the real world. I will also describe my recent research on security, privacy, and fairness problems in machine learning. I will conclude the talk by sketching interesting future directions for trustworthy machine learning.

报告人简介：

02

Yang Zhou

-Assistant Professor

-Auburn University

    Yang Zhou is an Assistant Professor in the Department of Computer Science and Software Engineering at the Auburn University. Prior to that, he received his Ph.D. degree in the College of Computing at the Georgia Institute of Technology. His current research interests lie in the areas of Trustworthy Machine Learning, Parallel, Distributed, and Federated Learning, Graph Machine Learning, and Natural Language Processing. He has published more than 80 research papers in top venues of machine learning (ICML, NeurIPS), data mining (KDD, ICDM, TKDD, DMKD, KAIS), artificial intelligence (AAAI, IJCAI, TIST), natural language processing (ACL, EMNLP), Web (WWW, TWEB), high performance computing (HPDC, SC), database systems (VLDB, ICDE, TKDE, VLDBJ), networking (JSAC, TOIT), web services (ICWS, TSC), and software engineering (ISSTA). The developed models and frameworks have been widely used by many research groups and six papers have been included in reading lists and taught in courses at universities worldwide. He was named as KDD Rising Star by Microsoft Academic Search and Microsoft Research Asia in 2016. The lab has built close collaborative relationships with Amazon, IBM, Microsoft, Sony, Baidu, and JD Research.

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