Zhanbo Wang | Searcharxiv

arXiv2026

Accelerating Confidential Databases with Crypto-free Mappings

Confidential databases (CDBs) enable secure queries over sensitive data in untrusted cloud environments using confidential computing hardware. While adoption is growing, widespread deployment is hindered by high overheads from frequent synchronous cryptographic operations, which cause significant computational and I/O bottlenecks. ZENO is a novel CDB design that removes cryptographic operations from the critical path. It introduces crypto-free mappings that maintain data-independent identifiers within the database while securely mapping them to plaintext secrets in a trusted domain. This paradigm shift yields substantial performance gains across industry-standard benchmarks (TPC-C, TPC-H) and a real-world industrial workload. Specifically, ZENO speeds up TPC-H queries by up to 53.1x on ARM S-EL2 and 94.7x on x86 TDX compared to HEDB. ZENO's optimization techniques have been integrated into GaussDB.

cs.CR↗

arXiv2025

LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning

Industrial Anomaly Detection (IAD) is critical for ensuring product quality by identifying defects. Traditional methods such as feature embedding and reconstruction-based approaches require large datasets and struggle with scalability. Existing vision-language models (VLMs) and Multimodal Large Language Models (MLLMs) address some limitations but rely on mask annotations, leading to high implementation costs and false positives. Additionally, industrial datasets like MVTec-AD and VisA suffer from severe class imbalance, with defect samples constituting only 23.8% and 11.1% of total data respectively. To address these challenges, we propose a reward function that dynamically prioritizes rare defect patterns during training to handle class imbalance. We also introduce a mask-free reasoning framework using Chain of Thought (CoT) and Group Relative Policy Optimization (GRPO) mechanisms, enabling anomaly detection directly from raw images without annotated masks. This approach generates interpretable step-by-step explanations for defect localization. Our method achieves state-of-the-art performance, outperforming prior approaches by 36% in accuracy on MVTec-AD and 16% on VisA. By eliminating mask dependency and reducing costs while providing explainable outputs, this work advances industrial anomaly detection and supports scalable quality control in manufacturing. Code to reproduce the experiment is available at https://github.com/LilaKen/LR-IAD.

cs.CV↗