||6月22日学术报告(Prof. Chunming Rong,Department of Electronic Engin 2016-08-15 01:35:45
报告题目：Privacy Preserving Data Service
报告人：Prof. Chunming Rong
报告人单位：Department of Electronic Engineering and Computer Science, University of Stavanger, Norway
Prof. Chunming Rong is head of the Center for IP-based Service Innovation (CIPSI) at the University of Stavanger (UiS) in Norway. The CIPSI has the mission to promote cross-fertilization between several research fields to facilitate design and delivery of large-scale and complex IP-based services required by many application areas. He is also visiting chair professor at Tsinghua University and served also as an adjunct professor at the University of Oslo 2005-2009. He spent one sabbatical year as visiting professor at the Stanford University 2009-2010. His research interests include cloud computing, big data analysis, security and privacy. He is co-founder and chairman of the Cloud Computing Association (CloudCom.org) and its associated conference and workshop series. He is member of the IEEE Study Group on Cloud Standard and co-chairs the IEEE Technical Area of Cloud Computing, in TCSC (Technical Committee on Scalable Computing). He is vice president of Cloud Security Alliance in Norway. He is the co-Editors-in-Chief of the Journal of Cloud Computing by Springer. He received award Editor's Choice in Discrete Mathematics in 1999. He coauthored a book titled "Security in Wireless Ad Hoc and Sensor Networks" published by John Wiley and Sons in 2009. Prof. Rong has extensive experience in managing research and development projects funded by both industry and funding agencies, such as the Norwegian Research Council.
摘要Abstract: With Big-data processing and analytic, organizations and enterprise have increased the collection of data from individuals, and are increasingly developing business models involving analytics to gain deep insights into the data collected. It is essential to release and merge data to third-parties for more extensive analytics for which an organization may not have the necessary expertise. Data has to be anonymized prior to such realease, to safeguard the privacy of individuals involved. While different algorithms with varying privacy guarantees have proposed for anonymizing data, large scale distributed anonymization remains an under-explored topic. We proposes a framework and a distributed anonymization remains an under-explored topic. We proposes a framework and a distributed algorithm for anonymization of large data sets. The work focuses on datacenter environments, both private data centers and public clouds; and is compatible with modern data analytics frameworks like map-reduce rand resilient distributed data sets (RDDs). We aim at minimizing identity, similarity and skins based attacks on anonymized data. In the proposed EU A4cloud project, we created solution to support users in deciding and tracking how their data is used by cloud service providers. By combining methods of risk analysis, policy enforcement, monitoring and compliance auditing with tailored IT mechanisms for security, assurance and redress.