Publications

2024

1. SIGCOMM’24

   ConfMask: Enabling Privacy-Preserving Configuration Sharing via Anonymization

   Yuejie Wang, Qiutong Men, Yao Xiao, and 2 more authors

   In Proceedings of the ACM SIGCOMM 2024 Conference, Sydney, NSW, Australia, 2024

   Abs DOI Bib HTML PDF Video Code

   Real-world network configurations play a critical role in network management and research tasks. While valuable, data holders of ten hesitate to share them due to business and privacy concerns. Existing methods are deficient in concealing the implicit information that can be inferred from configurations, such as topology and routing paths. To address this, we present ConfMask, a novel framework designed to systematically anonymize network topology and routing paths in configurations. Our approach tackles key privacy, utility, and scalability challenges, which arise from the strong dependency between different datasets and complex routing protocols. Our anonymization algorithm is scalable to large networks and effectively mitigates de-anonymization risk. Moreover, it maintains essential network properties such as reachability, waypointing and multi-path consistency, making it suitable for a wide range of downstream tasks. Compared to existing dataplane anonymization algorithm (i.e., NetHide), ConfMask reduces  75% specification differences between the original and the anonymized networks.

   @inproceedings{10.1145/3651890.3672217,
     author = {Wang, Yuejie and Men, Qiutong and Xiao, Yao and Chen, Yongting and Liu, Guyue},
     title = {ConfMask: Enabling Privacy-Preserving Configuration Sharing via Anonymization},
     year = {2024},
     booktitle = {Proceedings of the ACM SIGCOMM 2024 Conference},
     pages = {465--483},
     numpages = {19},
     keywords = {configuration sharing, topology anonymity, route anonymity},
     location = {Sydney, NSW, Australia},
     series = {ACM SIGCOMM '24},
     doi = {10.1145/3651890.3672217},
     url = {https://doi.org/10.1145/3651890.3672217},
   }

2. DASFAA’24

   LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval

   Shengbin Yue, Shujun Liu, Yuxuan Zhou, and 9 more authors

   In The 29th International Conference on Database Systems for Advanced Applications (DASFAA’24), 2024

   Abs DOI Bib HTML PDF Code Website

   We propose LawLLM, an LLM-powered intelligent legal system featuring on (1) Versatile Services: LawLLM provides a versatile diverse range of services through its multi-task capabilities; (2) Legal Reasoning: It is fine-tuned on supervised instruction data curated with legal syllogism prompting, enabling LawLLM to develop stronger legal reasoning capabilities based on clear judicial logics; (3) Verifiable Retrieval: with verifiable Labels, LawLLM can first distinguish relevant external knowledge, then incorporate and finally validate it, enhancing the quality and actuality of model output. A comprehensive legal benchmark, Law-Eval, is further constructed to evaluate intelligent legal systems from both objective and subjective dimensions. Experiments demonstrate the effectiveness of our system in serving various users across diverse legal scenarios.

   @inproceedings{10.48550/arXiv.2309.11325,
     author = {Yue, Shengbin and Liu, Shujun and Zhou, Yuxuan and Shen, Chenchen and Wang, Siyuan and Xiao, Yao and Li, Bingxuan and Song, Yun and Shen, Xiaoyu and Chen, Wei and Huang, Xuanjing and Wei, Zhongyu},
     title = {LawLLM: Intelligent Legal System with Legal Reasoning and Verifiable Retrieval},
     booktitle = {The 29th International Conference on Database Systems for Advanced Applications (DASFAA'24)},
     year = {2024},
     doi = {10.48550/arXiv.2309.11325},
   }

2023

1. arXiv

   Efficiently Visualizing Large Graphs

   Xinyu Li, Yao Xiao, and Yuchen Zhou

   2023

   Abs DOI Bib HTML PDF

   Most existing graph visualization methods based on dimension reduction are limited to relatively small graphs due to performance issues. In this work, we propose a novel dimension reduction method for graph visualization, called t-Distributed Stochastic Graph Neighbor Embedding (t-SGNE). t-SGNE is specifically designed to visualize cluster structures in the graph. As a variant of the standard t-SNE method, t-SGNE avoids the time-consuming computations of pairwise similarity. Instead, it uses the neighbor structures of the graph to reduce the time complexity from quadratic to linear, thus supporting larger graphs. In addition, to suit t-SGNE, we combined Laplacian Eigenmaps with the shortest path algorithm in graphs to form the graph embedding algorithm ShortestPath Laplacian Eigenmaps Embedding (SPLEE). Performing SPLEE to obtain a high-dimensional embedding of the large-scale graph and then using t-SGNE to reduce its dimension for visualization, we are able to visualize graphs with up to 300K nodes and 1M edges within 5 minutes and achieve approximately 10% improvement in visualization quality. Codes and data are available here.

   @misc{10.48550/arXiv.2310.11186,
     author = {Li, Xinyu and Xiao, Yao and Zhou, Yuchen},
     title = {Efficiently Visualizing Large Graphs},
     year = {2023},
     doi = {10.48550/arXiv.2310.11186},
   }