Yao Xiao

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.

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.