2025 International Conference on Intelligent Multimedia, Networking, and Security  

July 5-6^th, 2025, Qingdao, China

Keynote I

Title: Service-Centric Data Storage, Security and Robustness

Abstract:
The shared cloud is evolving into an integral part of IT infrastructure. The computing system is transforming from hardware-centric to service-centric solutions, where both data storage and computing are provided as services, which alleviates the need for end-users to purchase expensive hardware equipment. Currently, the majority of user data is stored with just a few centralized cloud data providers, which makes it vulnerable to unauthorized data access, censorship, and security breaches, as well as points of failure. While data encryption may be effective in addressing some security issues, it is unable to subdue the latter issue. As such, an innovative solution is required to ensure secure data storage. This talk will present our research on the design of distributed storage systems. Specifically, we propose a multi-layer parallel data processing infrastructure that can double node repair capability in hostile networks. Alternatively, it can increase the feasible data storage region by 50% in hostile networks.

Biography:
Dr. Jian Ren is a Professor in the Department of Electrical and Computer Engineering at Michigan State University. He received his Ph.D. degree in Communication and Electronic Systems from Xidian University, China. His current research interests include Al security, cybersecurity, distributed data sharing and storage, decentralized data management, blockchain-based e-voting, cloud computing security, and the Internet of Things. He is a recipient of the US National Science Foundation (NSF) CAREER award in 2009. Dr. Ren served as the TPC Chair of IEEE ICNC'17 and General Chair of ICNC'18. He has been the Executive Chair of ICNC since 2019. Dr. Ren has served as an Associate Editor for IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks (TOSN), and as Deputy Editor-in-Chief of IET Communications. Currently, he serves as the Editor-in-Chief of IET Communications, Associate Editor of IEEE Internet of Things Journal, and an IEEE VTS Distinguished Lecturer.

Keynote II

Title: Towards Explainable LLM Security and Evaluation

Abstract:
Recent years have witnessed the rapid advancement of Al, particularly driven by large language models (LLMs), with their applications permeating diverse domains. While LLMs flourish, their escalating challenges in security, privacy, and ethics have attracted significant concern. Ensuring the security and privacy of LLM systems while developing trustworthy, responsible Al technologies has become both a foundational prerequisite and essential safeguard for their sustainable application and industrial deployment. This talk focuses on LLM system security, discusses explainable techniques for enhancing LLM security, and presents a developed LLM security evaluation platform.

Biography:
Shouling Ji is a Qiushi Distinguished Professor in the College of Computer Science and Technology at Zhejiang University. He received a Ph.D. degree in Electrical and Computer Engineering from Georgia Institute of Technology, and a Ph.D. degree in Computer Science from Georgia State University. His current research interests include Al Security, Software and System Security, and Data-driven Security and Privacy. He is a member of ACM and IEEE, a senior member of CCF and was the Membership Chair of the IEEE Student Branch at Georgia State University (2012-2013). He was a Research Intern at the IBM T. J. Watson Research Center. Shouling is the recipient of the 2012 Chinese Government Award for Outstanding Self-Financed Students Abroad and 10 Best/Outstanding Paper Awards, including IEEE S&P 2025 Distinguished Paper Award and ACM CCS 2021 Best Paper Award.

Keynote III

Title: A Lightweight Deep Learning Solution to mmWave Human Activity Recognition

Abstract:
Millimeter wave (mmWave) based human activity recognition is vital in many smart loT applications. In practical IoT scenarios, fast and accurate human activity recognition is critically important. In this talk, we talk about a lightweight deep learning solution to human activity recognition based on the discrete Fourier transformation. The model has a fairly small number of model parameters while offering high accuracy in activity recognition. The core of the solution is a discrete Fourier transform module inside a neural network, which converts the temporal features of mmWave radar activity data into frequency features before a simple classifie performs activity recognition. The evaluation demonstrates that the DFT-based network can achieve the same accuracy as other traditional neural network models, but with a very small computational load.

Biography:
Dr. Shaoen Wu is the Department Chair and a full professor of Information Technology at Kennesaw State University. He also serves as a Steering Committee Chair of IEEE MMTC. Dr. Wu worked as the State Farm Endowed Chair Professor in the School of Information Technology at Illinois State University, served on the Advisory Council of Scholarship for the Vice Provost for Research, the Dean's Faculty Advisory Board and the assistant department chair of computer science at Ball State University, also worked as an assistant professor in the School of Computing at the University of Southern Mississippi, a Staff Scientist at ADTRAN, and a Member of Technical Staff at Bell Labs, Lucent Technologies. He has been a General or TPC Chair for several international conferences, including the CSM of Globecom 2021. Dr. Wu has directed research projects of several million dollars funded by US federal agencies and industry

Keynote IV

Title: A Succinct Range Proof for Polynomial-based Vector Commitment

Abstract:
A range proof serves as a protocol for the prover to prove to the veriffer that a committed number lies in a speciffed range, such as [0, 2^n), without disclosing the actual value. Range proofs find extensive application in various domains. To improve the scalability and efffciency, we propose MissileProof, a vector range proof scheme, proving that every element in the committed vector is within [0, 2^n). We first reduce this argument to a bi-to-univariate SumCheck problem and a bivariate polynomial ZeroTest problem. Then generalizing the idea of univariate SumCheck PIOP, we design a bi-to-univariate SumCheck PIOP. By introducing a random polynomial, we construct the bivariate polynomial ZeroTest using a univariate polynomial ZeroTest and a univariate polynomial SumCheck PIOP. Finally, combining the PIOP for vector range proof, a KZG-based polynomial commitment scheme and the Fiat-Shamir transformation, we get a zero - knowledge succinct non interactive vector range proof.

Biography:
Dr. Huaqun Wang is a full professor of Nanjing University of Posts and Telecommunications. He also serves as a director of Jiangsu Cryptography Technology Engineering Research Center. His research interests include applied cryptography, data security, blockchain, and cloud computing security.

Keynote V

Title: Full-duplex Underwater Magnetic Induction Communication: Opportunities and Challenges

Abstract:
This research focuses on Magnetic Induction (MI) communication and discusses the challenge of meeting high data rate demands as interest in MI-based underwater applications grows. The data rate in MI communication is limited by the use of a low operational frequency in generating a quasi-static magnetic field. In this research, we propose the use of full-duplex (FD) MI communication to efficiently utilize the available bandwidth and instantly double the data rate. We propose a two-dimensional (2D) transceiver architecture to enable full duplex communication by leveraging the directional nature of magnetic fields. We further evaluate the proposed end-to-end FD MI communication against self-interference (SI), its impact on communication distance, and orientation sensitivity. Finally, we conclude by discussing and highlighting the potential future research directions.

Biography:
Muhammad Muzzammil received the DEng degree in Information and Communication Engineering from the College of Underwater Acoustic Engineering, Harbin Engineering University (HEU), China in 2021. He received the Best Poster Award at the 13th ACM International Conference on Underwater Networks & Systems (WUWNet' 18) and is also being selected in the IEEE OES Student Poster Competitions (OCEANS'19 & OCEANS' 23). He previously worked as a visiting scholar at Hamad Bin Khalifa University, Doha, Qatar, and as a remote intern at the Information Systems Lab (ISL), King Abdullah University of Science and Technology. KSA. Currently, he is working as a postdoctoral researcher at HEU, China. His research interests lie in the areas of wireless communications, magneto - inductive communication, and underwater acoustic communication and networking.