沈鸿教授学术报告会-Privacy-preserving Data Sharing against Malicious Attacks
发布时间: 2021-04-23 浏览次数: 10
 

报告题目Privacy-preserving Data Sharing against Malicious Attacks

时间4月29日上午 10:30

地点图书馆一楼报告厅

报告人:沈鸿

个人简介:沈鸿,中山大学高层次引进人才,计算机科学与工程学院教授,国家特聘专家,中国科学院“百人计划”入选者。历任中山大学教授和先进计算研究所所长,北京交通大学教授和先进计算研究所所长,澳大利亚阿德莱德大学计算机科学首席教授、并行分布式系统和网络研究中心主任、工学部副部长,日本先端科学技术大学教授和计算机网络实验室主任,澳大利亚格里菲斯大学计算机科学首席教授和并行计算研究中心研究主任。

研究领域:沈鸿教授主要研究领域为并行与分布式计算、数据保护和安全计算、高性能网络、数据挖掘,致力于面向现代信息化社会各类应用问题的发现、建模、求解、分析和性能评估。在国际主要学术刊物和会议上发表了400余篇文章,包括100多篇发表在国际一流刊物上。主持过十多个澳洲、日本和中国国家自然科学基金项目,获得过中国科学院自然科学、教育部科技进步奖、格里菲斯大学和日本先端科学技术大学学术贡献特别奖。担任过10余个国际学术刊物的编委, IEEE Task Force for Cluster Computing 执行委员会委员, IEEE Technical Committee for Parallel Processing执行委员会委员和澳太地区负责人,多个国际学术会议的大会主席和程序委员会主席。

报告内容Recent years have seen a remarkable development of privacy-preserving computing (PPC) as an effective way to achieve secure distributed computing and information sharing across a public network that are of critical importance for network-centric computation and big data analytics. Recent advancement in cloud data sharing techniques has made traditional PPC techniques unable to effectively against emerging malicious attacks such as shilling, collusion and inference attacks.

In the first part of this talk, I will first introduce the problem of privacy-preserving computing, its research challenges in cloud big data computing, then give a taxonomy on data protection techniques categorized on the security levels of data publishing, with the focus on differential privacy as an effective method to combat malicious attacks, and provide an overview on our contributions in privacy-preserving computing. In the second part, to show the power of differential privacy for secure data sharing, I will give two examples of our work in applying differential privacy to achieve privacy-preserving recommendation and data clustering against collusion and inference attacks. Finally I will conclude the talk by displaying our ongoing research projects.