题目：Security, privacy, and blockchain research in deep learning and the Internet of Things
主讲人：Jun Zhao Assistant Professor，Nanyang Technological University (NTU)
时间： 10:00am - 12:00am
摘要：Local differential privacy (LDP) is a strong privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Jun's paper proposes novel LDP mechanisms which outperform existing solutions regarding worst-case noise variance. The proposed solutions are further used to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many machine learning tasks such as linear regression, logistic regression, and support vector machines (SVM) classification. Experiments on real datasets confirm the effectiveness of the proposed methods, and their advantages.
个人介绍：Jun Zhao is currently an Assistant Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU) in Singapore. He received a PhD degree in Electrical and Computer Engineering from Carnegie Mellon University (CMU) in the USA (advisors: Virgil Gligor, Osman Yagan; collaborator: Adrian Perrig) and a bachelor's degree from Shanghai Jiao Tong University in China. Before joining NTU first as a postdoc with Xiaokui Xiao and then as a faculty member, he was a postdoc at Arizona State University as an Arizona Computing PostDoc Best Practices Fellow (advisors: Junshan Zhang, Vincent Poor). His research interests include blockchains, security, and privacy with applications to the Internet of Things and deep learning. In terms of publications, he has over a dozen journal articles published/accepted in IEEE/ACM Transactions as well as over twenty conference/workshop papers. One of his first-authored papers was shortlisted for the best student paper award in IEEE International Symposium on Information Theory (ISIT) 2014, a prestigious conference in information theory.