The Eighth International Workshop on Security in Blockchain and Cloud Computing (SBC)

This talk highlights challenges and opportunities for trustworthy AI with a focus on privacy attacks and countermeasures. AI and machine learning have no future if their privacy and security concerns are not addressed. Machine learning models could hide malicious code or backdoors, and leak private information about users. We will explore inference attacks against machine learning models and frameworks (e.g., federated learning), and set out the requirements for privacy-preserving AI systems.

Giuseppe Ateniese is the Farber Endowed Chair in Computer Science and department chair at Stevens Institute of Technology. He was with Sapienza-University of Rome (Italy) and Assistant/Associate Professor at Johns Hopkins University (USA), and one of the founders of the JHU Information Security Institute. He was a researcher at IBM Zurich Research lab (Switzerland) and scientist at the Infor- mation Sciences Institute of the University of Southern California (USA). He also briefly worked as visiting professor at Microsoft in Redmond (USA). He received the NSF CAREER Award for his research in privacy and security, and the Google Faculty Research Award, the IBM Faculty Award, and the IEEE CISTC Technical Recognition Award for his research on cloud security. He has contributed to areas such as proxy re-cryptography, anonymous communication, two-party computation, secure storage, and provable data possession. He is currently working on cloud security and machine learning applied to security and intelligence issues for which he received an IBM SUR Award. He is also investigating new security applications for decentralized computing based on the blockchain/bitcoin technology.

We increasingly live in a software-defined world where systems that were once implemented as rigid control capabilities or fixed function hardware systems are now highly programmable through software interfaces that decouple underlying details and offer remote control and centralized management. Blockchain and cloud computing systems are early examples of embracing software-defined system designs.

We argue that we should further leverage software-defined principles to make security programmable, thus also make this software-defined world more secure. We will use two case studies to demonstrate how programmable security can be achieved on software-defined networking (SDN) and blockchain systems, respectively.

Dr. Guofei Gu is a professor in the Department of Computer Science & Engineering at Texas A&M University (TAMU). Before coming to Texas A&M, he received his Ph.D. degree in Computer Science from the College of Computing, Georgia Institute of Technology. His research interests are in network and systems security.

Dr. Gu is a recipient of 2010 NSF CAREER Award, 2013 AFOSR Young Investigator Award, 2010 IEEE S&P Best Student Paper Award, 2015 ICDCS Best Paper Award, Texas A&M Dean of Engineering Excellence Award, and Presidential Impact Fellow Award. He is an active member of the security research community and has pioneered several new research directions such as botnet detection/defense and SDN security. Dr. Gu has frequently served on the program committees of top-tier security conferences such as IEEE S&P, ACM CCS, USENIX Security, and NDSS. He is an IEEE Fellow, an ACM Distinguished Member, an Associate Editor for IEEE Transactions on Information Forensics and Security (T-IFS), and the Steering Committee co-chair for SecureComm. He recently co-chaired the 2018 NSF Workshop on Programmable System Security in a Software Defined World. He is currently directing the SUCCESS Lab at TAMU.