Seminar: Privacy and Trust in Data-Centric Systems: A Cryptographic Approach
Thang Hoang
Assistant Professor of Computer Science
Virginia Tech
Friday, November 6, 2026
2:30 - 3:30p.m.
3100 Torgersen Hall
Abstract
Data-centric systems enable us to store information remotely, search public repositories, and train and use AI models. These capabilities raise two fundamental questions: what can a service learn about our data and interactions, and how can we verify that it behaves as expected? This talk presents a cryptographic perspective on these questions through recent research spanning private data, public data, and analytics. I will discuss techniques for verifying the retrievability of outsourced data, ensuring the searchability of encrypted data, and retrieving public information without revealing which records are requested. I will then turn to trustworthy data analytics, with special emphasis on recent advancements in trustworthy and privacy-preserving AI, including federated learning and cryptographically verifiable AI inference. Throughout, I will present the high-level ideas, trade-offs between security and efficiency, and remaining research challenges. The talk is intended for a broad computer science audience and does not assume prior expertise in cryptography.
Biography
Thang Hoang is an Assistant Professor in the Department of Computer Science at Virginia Tech. His research focuses on applied cryptography, privacy-enhancing technologies, trustworthy AI, and verifiable computation, with an emphasis on developing provably secure and practically efficient techniques for data systems. He received his Ph.D. in Computer Science from the University of South Florida in 2020 and was a postdoctoral scholar at Carnegie Mellon University before joining Virginia Tech in 2021.