SoReMo: An End-to-End Security and Privacy Framework for Big Data and Machine Learning
Murat Kantarciogl, Cybersecurity Lab, University of Texas at Dallas
Recent cyberattacks have shown that the leakage/stealing of big data may result in enormous monetary loss and damage to organizational reputation, and increased identity theft risks for individuals. Furthermore, in the age of big data, protecting the security and privacy of stored data is paramount for maintaining public trust, accountability and getting the full value from the collected data. Therefore, we need to address security and privacy challenges ranging from allowing access to big data to building novel data analytics model using the privacy sensitive data. In this talk, we will provide an overview of our end-to-end solution framework that tries to address these challenges.
We start the talk by discussing the unique security and privacy challenges arise due to big data and the recent systems designed to analyze big data. Later on, we discuss how to add additional security layer for protecting big data using encryption techniques. Especially, we discuss our work on leveraging the modern hardware based trusted execution environments such as Intel SGX for secure encrypted data processing. We focus on how to provide a simple, secure and high level language based framework that is suitable for enabling generic data analytics for non-security experts.
Also, we discuss our work on addressing the security and privacy issues with respect to the resulting data analytics/machine learning (ML) models. First, we discuss how these learned machine ML models could be attacked, how a game theoretic solution concept could be used to learn more robust ML models resistant to various attacks. In addition, we discuss how to build more robust models for federated learning systems. Finally, we discuss why the perceived fragility of the ML models against certain attacks is useful for enhancing individual privacy by showing how to look smarter to a ML model by modifying your social media profile.
This forum is part of the SoReMo Initiative.
Dr. Murat Kantarcioglu is a Professor in the Computer Science Department and Director of the Data Security and Privacy Lab at The University of Texas at Dallas (UTD). He received a PhD in Computer Science from Purdue University in 2005 where he received the Purdue CERIAS Diamond Award for Academic excellence. He is also a visiting scholar at Harvard Data Privacy Lab. Dr. Kantarcioglu's research focuses on the integration of cyber security, data science and blockchains for creating technologies that can efficiently and securely process and share data.
His research has been supported by grants including from NSF, AFOSR, ARO, ONR, NSA, and NIH. He has published over 170 peer reviewed papers in top tier venues such as ACM KDD, SIGMOD, ICDM, ICDE, PVLDB, NDSS, USENIX Security and several IEEE/ACM Transactions as well as served as program co-chair for conferences such as IEEE ICDE, ACM SACMAT, IEEE Cloud, ACM CODASPY. Some of his research work has been covered by the media outlets such as the Boston Globe, ABC News, PBS/KERA, DFW Television, and has received multiple best paper awards. He is the recipient of various awards including NSF CAREER award, the AMIA (American Medical Informatics Association) 2014 Homer R Warner Award and the IEEE ISI (Intelligence and Security Informatics) 2017 Technical Achievement Award presented jointly by IEEE SMC and IEEE ITS societies for his research in data security and privacy. He is also a fellow of AAAS and distinguished scientist of ACM.Register for link