DIMACS/RUCIA Workshop on Information Assurance in the Era of Big Data

February 6, 2014
DIMACS Center, CoRE Building, Rutgers University

Thu Nguyen, Rutgers University, tdnguyen at cs.rutgers.edu
Manish Parashar, Rutgers University, parashar at caip.rutgers.edu
Hoang Pham, Rutgers University, hopham at rci.rutgers.edu
Rebecca Wright, Rutgers University, rebecca.wright at rutgers.edu
Hui Xiong, Rutgers University, hxiong at rutgers.edu
Presented under the auspices of the DIMACS Special Focus on Algorithmic Foundations of the Internet, the DIMACS Special Focus on Cybersecurity, and The Rutgers Center for Information Assurance (RUCIA), with additional support from the National Science Foundation under grant number DGE-1241315.
Workshop Announcement

This workshop is designed to provide a forum for Information Assurance researchers and practitioners to share their research, experiences, and challenges in the Big Data era. Complex, heterogeneous, dynamic big data are generated from the Internet, social media, retail, finance, healthcare, and more. This data carries with it both information assurance challenges and information assurance opportunities. For example, can be used to help companies address many of the enterprise IT security issues they face today. It can help them understand activities on their networks in near real-time, giving them the ability to make better decisions on information assurance. Leveraging big data in an organization's information assurance program requires an infrastructure capable of capturing viable, time-sensitive information on both the infrastructure's overall state and its internal operations. Conversely, big data itself presents information assurance challenges: data must be collected and maintained in a way that protects its confidentiality and integrity, and must be used in a way that addresses privacy concerns of data subjects.

This workshop will provide the scientific and industry community a dedicated forum for discussing state-of-the-art research on big data analysis, mining, understanding, learning, and security, as well as the data management architecture and visualization of very large data sets.

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Document last modified on November 13, 2014.