To authors: A regular paper presentation would be 30 mins in total (including 5 mins Q&A).
Recent advances in cloud services and outsourced computation provide a promising paradigm for applications that generate, collect or process large amounts of sensitive data. However, they introduce significant security and privacy issues. Among others, ensuring proper access control and trust on the infrastructure is a crucial challenge. In this talk, I will overview the challenges and solutions related to attribute-based encryption for access control and related transparency issues. I will present some of our recent work related to integrated privacy-preserving user-centric access control supporting secure deduplication to address key security and privacy challenges in cloud services. Such attribute-based encryption approaches, as well as other emerging cryptographic mechanisms for secure computation typically employ a third-party authority (TPA) as an integral component that need to be trusted. Recent work on certificate transparency approaches provides a promising direction to address such general trust issues related to a TPA. We will present our recent work tailored towards such authority transparency issues and discuss challenges ahead.
James Joshi is a professor of School of Computing and Information at the University of Pittsburgh, and the director/founder of the Laboratory of Education and Research on Security Assured Information Systems (LERSAIS). He is currently serving as an NSF Program Director in the Computer and Network System (CNS) division and in the Secure and Trustworthy Cyberspace (SaTC) program. He is an elected Fellow of the Society of Information Reuse and Integration (SIRI), a Senior member of the IEEE and a Distinguished Member of the ACM. His research interests include access control models, security and privacy of distributed systems, trust management and network security. He is a recipient of the NSF CAREER award in 2006. He established and managed the NSF CyberCorp Scholarship for Service program at Pitt in 2006. He has served as program co-chair and/or general co-chair of several international conferences/workshops, including, ACM SACMAT, IEEE BigData, IEEE IRI, IEEE CIC, IEEE ISM, IEEE EDGE, etc. He currently serves as the steering committee chair of IEEE CIC/TPS/CogMI. Currently, he is the EIC of the IEEE Transactions on Services Computing. He had also served in or is in the editorial board of several international journals. He has published over 120 articles as book chapters and papers in journals, conferences and workshops, and has served as a special issue editor of several journals including Elsevier Computer & Security, ACM TOPS, Springer MONET, IJCIS, and Information Systems Frontiers. His research has been supported by NSF, NSA/DoD, and Cisco. Earlier in 1995, he had led the efforts to establish the first Computer Science & Engineering undergraduate degree program in Nepal.
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks (DNNs), are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, healthcare, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences. Interest in this area of research has simply exploded. In this work, we will cover the state-of-the-art in trustworthy machine learning, and then cover some interesting future trends.
Somesh Jha received his B.Tech from Indian Institute of Technology, New Delhi in Electrical Engineering. He received his Ph.D. in Computer Science from Carnegie Mellon University under the supervision of Prof. Edmund Clarke (a Turing award winner). Currently, Somesh Jha is the Lubar Professor in the Computer Sciences Department at the University of Wisconsin (Madison). His work focuses on analysis of security protocols, survivability analysis, intrusion detection, formal methods for security, and analyzing malicious code. Recently, he has focussed his interested on privacy and adversarial ML (AML). Somesh Jha has published several articles in highly-refereed conferences and prominent journals. He has won numerous best-paper and distinguished-paper awards. Prof Jha also received the NSF career award. Prof. Jha is the fellow of the ACM and IEEE.