Big Data Security and Privacy: Developments and Directions

Prof. Bhavani Thuraisingham
The University of Texas at Dallas, USA

Abstract:
The collection, storage, manipulation and retention of massive amounts of data have resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. For example, even if personally identifiable information is removed from the data, when data is combined with other data, an individual can be identified. This is essentially the inference and aggregation problem that data security researchers have been exploring for the past four decades. This problem is exacerbated with the management of big data as different sources of data now exist that are related to various individuals. While collecting massive amounts of data causes security and privacy concerns, big data analytics applications in cyber security is exploding. For example, an organization can outsource activities such as identity management, email filtering and intrusion detection to the cloud. This is because massive amounts of data are being collected for such applications and this data has to be analyzed. The question is, how can the developments in big data management and analytics techniques be used to solve security problems? These problems include malware detection, insider threat detection, and intrusion detection. To address the challenges of big data security and privacy as well as big data analytics for cyber security applications, we organized a workshop sponsored by the National Science Foundation in September 2014 and presented the results in 2015 at an inter-agency workshop in Washington DC. Since then several developments have been reported on big data security and privacy as well as on big data analytics of cyber security. This talk will summarize the findings of the workshop and discuss the developments and directions.

Biography:
Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor in the Erik Jonsson School of Engineering and Computer Science at the University of Texas, Dallas (UTD) and the executive director of UTD’s Cyber Security Research and Education Institute. Her current research is on integrating cyber security, cloud computing and data science. Prior to joining UTD she worked at the MITRE Corporation for 16 years including a three-year stint as a Program Director at the NSF. She initiated the Data and Applications Security program at NSF and was part of the Cyber Trust theme. Prior to MITRE, she worked for the commercial industry for six years including Honeywell. She is the recipient of numerous awards including the IEEE CS1997 Technical Achievement Award, the ACM SIGSAC 2010 Outstanding Contributions Award, 2012 SDPS Transformative Achievement Gold Medal, 2013 IBM Faculty Award, 2017 ACM CODASPY Research Award, and 2017 IEEE CS Services Computing Technical Committee Research Innovation Award. She is a 2003 Fellow of the IEEE and the AAAS and a 2005 Fellow of the British Computer Society. She has published over 120 journal articles, 250 conference papers, 15 books, has delivered over 130 keynote addresses, and is the inventor of five patents. She has chaired conferences and workshops for women in her field including on Women in Cyber Security, Women in Data Science, and Women in Services Computing/Cloud and has delivered featured addresses at SWE, WITI, and CRA-W.

Keynote : Big Data 2.0 Processing Engines

Prof. Sherif Sakr
King Saud bin Abdulaziz University for Health Sciences

Abstract:
For a decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created a lot of momentum such that it has become the defacto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions which constituted a new wave of mostly domain-specific, optimized big data processing engines. In this talk, we refer to this new wave of systems as "Big Data 2.0 processing systems". We provide a taxonomy and analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.

Biography:
Prof. Sherif Sakr is currently a Professor of Computer Science at King Saud bin Abdulaziz University for Health Sciences. He is also affiliated with The School of Computer Science and Engineering (CSE) at University of New South Wales (UNSW Australia) and Data61/CSIRO (formerly NICTA). He received his PhD degree in Computer and Information Science from Konstanz University, Germany in 2007. Dr. Sakr held a visiting Researcher/Professor appointments in international reputable research and academic institutes including Macquarie University (2008-2009), Microsoft Research, Redmond, USA (2011), Nokia Bell Labs, Ireland - Formerly Alcatel-Lucent Bell Labs (2012), Humboldt-Universität zu Berlin, Germany (2015), University of Zurich, Switzerland (2016), Technical University of Dresden, Germany (2016). In 2013, Sherif has been awarded the Stanford Innovation and Entrepreneurship Certificate. Dr. Sakr's research interest is data and information management in general, particularly in big data processing systems, big data analytics, data science and big data management in cloud computing platforms. He is an associate editor of the cluster computing journal and Transactions on Large-Scale Data and Knowledge-Centered Systems (TLDKS). He is also an editorial board member of many reputable international journals. Dr. Sakr is an IEEE Distinguished Speaker