arXiv subjects

Xinjie Zhou

Publications and source records attributed to Xinjie Zhou.

4 recordsLinked to original sources

arXiv2026

Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning

Large language models (LLMs) exhibit powerful capabilities but risk memorizing sensitive personally identifiable information (PII) from their training data, posing significant privacy concerns. While machine unlearning techniques aim to remove such data, they predominantly depend on access to the training data. This requirement is often impractical, as training data in real-world deployments is commonly proprietary or inaccessible. To address this limitation, we propose Data-Free Selective Unlearning (DFSU), a novel privacy-preserving framework that removes sensitive PII from an LLM without requiring its training data. Our approach first synthesizes pseudo-PII through language model inversion, then constructs token-level privacy masks for these synthetic samples, and finally performs token-level selective unlearning via a contrastive mask loss within a low-rank adaptation (LoRA) subspace. Extensive experiments on the AI4Privacy PII-Masking dataset using Pythia models demonstrate that our method effectively removes target PII while maintaining model utility.

cs.CR↗

arXiv2025

A Universal Scheme for Dynamic Partitioned Shortest Path Index: Survey, Improvement, and Experiments

Shortest Path (SP) computation is a fundamental operation in many real-life applications such as navigation on road networks, link analysis on social networks, etc. These networks tend to be massive, and graph partitioning is commonly leveraged to scale up the SP algorithms. However, the Partitioned Shortest Path (PSP) index has never been systematically investigated. Moreover, few studies have explored its index maintenance in dynamic networks. In this paper, we survey the dynamic PSP index and propose a universal scheme for its design and analysis. Specifically, we first review the SP algorithms and put forward a novel structure-based partition method classification to facilitate the selection of partition methods. Furthermore, we summarize the existing Pre-boundary PSP strategy and propose two novel strategies (No-boundary and Post-boundary) to improve its index performance. Lastly, we propose a universal scheme with three dimensions (SP algorithm, partition method, and PSP strategy) to facilitate the analysis and design of the PSP index. Based on this scheme, we put forward five new PSP indexes with a prominent query or update efficiency performance. Extensive experiments are conducted to evaluate the performance of the PSP index and the effectiveness of the proposed techniques, with valuable guidance on the PSP index design.

cs.DB↗

arXiv2025

High Throughput Shortest Distance Query Processing on Large Dynamic Road Networks

Shortest path (SP) computation is the building block for many location-based services, and achieving high throughput SP query processing with real-time response is crucial for those services. However, existing solutions can hardly handle high throughput queries on large dynamic road networks due to either slow query efficiency or poor dynamic adaption. In this paper, we leverage graph partitioning and propose novel Partitioned Shortest Path (PSP) indexes to address this problem. Specifically, we first put forward a cross-boundary strategy to accelerate the query processing of PSP index and analyze its efficiency upper bound theoretically. After that, we propose a non-trivial Partitioned Multi-stage Hub Labeling (PMHL) that subtly aggregates multiple PSP strategies to achieve fast index maintenance and consecutive query efficiency improvement during index update. Lastly, to further optimize throughput, we design tree decomposition-based graph partitioning and propose Post-partitioned MHL (PostMHL) with faster query processing and index update. Experiments on real-world road networks show that our methods outperform state-of-the-art baselines in query throughput, yielding up to 2 orders of magnitude improvement.

cs.DB↗

arXiv2023

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model

Most biomedical pretrained language models are monolingual and cannot handle the growing cross-lingual requirements. The scarcity of non-English domain corpora, not to mention parallel data, poses a significant hurdle in training multilingual biomedical models. Since knowledge forms the core of domain-specific corpora and can be translated into various languages accurately, we propose a model called KBioXLM, which transforms the multilingual pretrained model XLM-R into the biomedical domain using a knowledge-anchored approach. We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora. Then we design three corresponding training tasks (entity masking, relation masking, and passage relation prediction) and continue training on top of the XLM-R model to enhance its domain cross-lingual ability. To validate the effectiveness of our model, we translate the English benchmarks of multiple tasks into Chinese. Experimental results demonstrate that our model significantly outperforms monolingual and multilingual pretrained models in cross-lingual zero-shot and few-shot scenarios, achieving improvements of up to 10+ points. Our code is publicly available at https://github.com/ngwlh-gl/KBioXLM.

cs.CL↗