地点:清华大学FIT楼 3-225

演讲人:张宇鹏  加州大学伯克利分校博士后





With the rapid development of rising techniques such as machine learning and blockchain, data privacy becomes a big concern. Companies are collecting more and more data from users so as to run machine-learning algorithms on that data to develop products and services. Users’ data are posted publicly on the blockchain for others to validate and reach consensus. Despite of the great benefits of these techniques, they currently require users to give up control of their data and to trade off integrity and privacy for utility. In this talk, I will discuss several cryptographic techniques I have developed to address these issues. I will first talk about privacy-preserving machine learning, which allows companies to execute machine-learning algorithms without learning users’ data. I will then discuss about techniques for verifiable computation and zero knowledge proof that can be used to ensure the correctness of computations without leaking information about the underlying data. 



Yupeng Zhang is an Assistant Professor in Computer Science and Engineering Department of Texas A&M University starting Fall 2019. He is currently a postdoctoral researcher at UC Berkeley working with Professor Dawn Song. His research is focused on applied cryptography, and his work on privacy-preserving machine learning, zero knowledge proof, verifiable computation and searchable encryption has been published at top security conferences. He is a recipient of Google PhD Fellowship and Distinguished Dissertation Award of ECE, University of Maryland. 

张宇鹏教授2019年秋季加入德州农工大学(TAMU塔木)计算机系开始助理教授职位,现在加州大学伯克利分校Dawn Song教授组进行博士后研究。他主要的研究方向是应用密码学和安全。他在多方安全计算的机器学习,零知识证明,可验证计算和可搜索加密方面有多篇论文发表在顶级安全会议上。他在博士期间获得谷歌博士奖学金和马里兰电子工程系最佳博士论文奖。

联系人:段海新, duanhx@tsinghua.edu.cn



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