8 Dec - Bingsheng Zhang - Publicly Verifiable MPC & Its Applications

Title: Publicly Verifiable MPC & Its Applications

Abstract: Secure multi-party computation (MPC) becomes increasingly efficient in recent years. In practice, MPC platforms are typically used in the clients-servers setting, where many clients provide their data to a MPC system performed by a few servers. Both privacy of the clients' data and the correctness of the computation are ensured as long as there is a single honest server. However, in a conventional MPC, nothing can be guaranteed when all the servers are compromised and colluding to each other. What is even worse is that such a security breach is difficult to detect by outside users. Publicly verifiable MPC aims to resolve this issue by allowing any third party to audit the computational integrity even if all the servers are corrupted. This talk will cover recent break-through in practical publicly verifiable MPC and its applications in several security sensitive domains.

NB: Cryptographic background is not necessary to follow this talk.

Bio: Dr. Bingsheng Zhang is a lecturer in the School of Computing and Communications at Lancaster University and a member of the Security Lancaster research centre. He is currently an active investigator in the EPSRC PETRAS IoT Hub on Security, Privacy and Trust (EP/N023234/1). Dr. Zhang specialises in cryptography and cyber security. He has published over 30 papers in peerreviewed security/cryptography conferences and journals, including the world’s leading ones, such as EUROCRYPT, ASIACRYPT, ACM CCS, ICDCS, FC, SCN, INFOCOM, IEEE Trans. Mob. Comput., and IEEE Trans. Inf. Forensics Security. In the recent years, his research efforts mainly focus on verifiable cloud outsourcing, secure multi-party computation and privacy preserving data mining. During 2009 – 2011, he was a researcher at Cybernetica AS, where he was dedicated to the design and development of a privacy preserving data-mining platform called Sharemind. This work was later granted by the Defense Advanced Research Projects Agency (DARPA). For the related works, during his post-doc at State University of New York, Buffalo, he developed a proof-carrying cloud outsourcing system for all convex optimization problems and a privacy-aware cloud-assisted healthcare monitoring system via compressive sensing. Recently, he has initiated the works of crowd verifiable secure multi-party computation and privacy preserving stylometry.