The advent of novel applications such as smart city infrastructure and large-language models training is driven by the proliferation of big data. This workshop aims to study a comprehensive framework that seamlessly integrates scalable big data fusion and computing engines with privacy-preserving techniques and cryptographic methods. Systems are to process massive and heterogeneous datasets while enforcing strict privacy constraints, enabling secure analysis and collaboration on sensitive information.

This workshop provides a unique forum for in-depth discussion. We welcome submissions that are aligned with the workshop's thematic. Papers, consisting of only a paper title and an abstract, should be sent to jianqiu@nuaa.edu.cn. Please note that accepted papers are for presentation and discussion only and will not be included in the official conference proceedings.

9:00 – 12:00, February 2 (Monday), 2026

Chair: Jianqiu Xu (Nanjing University of Aeronautics and Astronautics)

• Simulation Intelligent Fusion for Industrial Applications

Haisheng Li (Beijing University of Business and Technology)

• Efficient Distributed Mini-batch GNN Training with Decentralized Batch Processing

Yingxia Shao (Beijing University of Posts and Telecommunications‌)

• High-Performance General-Purpose Data Processing with Ray Tracing Cores

Kai Zhang (Fudan University)

• Towards Trustworthy Graph Analytics with Cohesive Structures

Kai Wang (Shanghai Jiao Tong University)

• Multimodal Data Fusion in Urban Computing: From Deep Learning to Large Models

Yuxuan Liang (Hong Kong University of Science and Technology (Guangzhou))