Efficient Encrypted Data Analysis Using Trusted Execution Environment
COM2 Level 4
Executive Classroom, COM2-04-02
Abstract:
Recent emergence of cloud computing has enabled organization to outsource their data to the cloud for simplifying data management. At the same time, the security of the potentially sensitive data stored in servers managed by cloud service providers became an important concern. To address such concerns, encrypting data stored in remote, third party cloud servers emerged as an important option. Still, storing encrypted data created challenges with respect to efficient data analysis. In this talk, we first discuss our work on how to do similarity search over the encrypted data using searchable encryption schemes and discuss potential attacks against searchable encryption schemes that leverage certain side channel information such as data access patterns.
Later on, we discuss how to leverage the modern hardware based trusted execution environments (TEEs) such as Intel SGX for secure encrypted data processing. Especially, we discuss how to provide a simple, secure and high level language based framework that is suitable for enabling generic data analytics for non-security experts who do not have security concepts such as "oblivious execution". Our proposed framework allows data scientists to perform the data analytic tasks with TEEs using a Python/Matlab like high level language; and automatically compiles programs written in our language to optimal execution code by managing issues such as optimal data block sizes for I/O, vectorized computations to simplify much of the data processing, and optimal ordering of operations for certain tasks. Using these design choices, we show how to provide guarantees for efficient and secure data analytics. Our empirical results indicate that our proposed framework is orders of magnitude faster than the general oblivious execution alternatives.
Biodata:
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 since 2013. Dr. Kantarcioglu's research focuses on the integration of cyber security, data science and blockchains, 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, IEEE ICDM, ICDE, PVLDB, NDSS, USENIX Security and several IEEE/ACM Transactions as well as served as program chair for conferences such as 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 Distinguished Scientist of ACM.