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arXiv 2024年

作者： Chen, Mouxiang Tian, Hao Liu, Zhongxin Ren, Xiaoxue Sun, Jianling The State Key Laboratory of Blockchain and Data Security Zhejiang University China

While existing code large language models (code LLMs) exhibit impressive capabilities in code generation, their autoregressive sequential generation inherently lacks reversibility. This limitation hinders them from timely correcting previous missing statements during coding as humans do, often leading to error propagation and suboptimal performance. We introduce JUMPCODER, a novel model-agnostic framework that enables human-like online modification and non-sequential generation to augment code LLMs. The key idea behind JUMPCODER is to insert new code into the currently generated code when necessary during generation, which is achieved through an auxiliary infilling model that works in tandem with the code LLM. Since identifying the best infill position beforehand is intractable, we adopt an infill-first, judge-later strategy, which experiments with filling at the k most critical positions following the generation of each line, and uses an Abstract Syntax Tree (AST) parser alongside the Generation Model Scoring to effectively judge the validity of each potential infill. Extensive experiments using six state-of-the-art code LLMs across multiple and multilingual benchmarks consistently indicate significant improvements over all baselines. Our code is public at https://***/keytoyze/JumpCoder. Copyright © 2024, The Authors. All rights reserved.

关键词： Trees (mathematics)

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In Situ Neural Relational Schema Matcher

In Situ Neural Relational Schema Matcher

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International Conference on data Engineering

作者： Xingyu Du Gongsheng Yuan Sai Wu Gang Chen Peng Lu The State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The scarcity of training data restricts a neural network from capturing schema diversity and intricacies, hindering schema-matching models' generalization capabilities. In this paper, we propose ISResMat, a framework specifically designed to match the schemas of relational tables by fine-tuning a pre-trained language model. We first offer a training data construction method, Pairwise Sampling, which could generate the training dataset with table data. Next, we design two loss functions (i.e., Meta-Matching Loss and Agent-Delegating Loss) to learn representations of table columns. With those representations, we could calculate matching scores between different table columns for deducing the matching candidates, which provides a novel approach to schema matching. Finally, we present two optimizations (i.e., Matching Rectification Loss and Distribution-Aware Fingerprint) to handle the problems of matching cardinality constraints and numerical columns, respectively. ISResMat is a flexible framework supporting instance-based, schema-based, and hybrid matching without significant modification. Experiments on 500+ fabricated and human-curated relation pairs spanning diverse domains and matching scenarios showcase that our approach outperforms existing state-of-the-art methods.

关键词： Training Federated learning databases Neural networks Training data Fingerprint recognition Sampling methods

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COGSQL: A Cognitive Framework for Enhancing Large Language Models in Text-to-SQL Translation 39

COGSQL: A Cognitive Framework for Enhancing Large Language M...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yuan, Hongwei Tang, Xiu Chen, Ke Shou, Lidan Chen, Gang Li, Huan The State Key Laboratory of Blockchain and Data Security Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China

ISBN: (纸本)157735897X

Large language models (LLMs) have significantly advanced the performance of various natural language processing tasks, including text-to-SQL. Current LLM-based text-to-SQL schemes mainly focus on improving the understanding of natural language questions (NLQs) or refining the quality of generated SQLs. While these strategies are effective, they often address specific, nuanced aspects. In contrast, humans approach text-to-SQL with a holistic view, applying transitional logical reasoning across multiple steps to arrive at the final answer. We believe LLMs can leverage human cognitive processes to achieve greater accuracy in text-to-SQL. In this paper, we present COGSQL, a framework featuring a suite of tailored models and strategies aimed at replicating human cognitive processes for enhanced LLM-based text-to-SQL. COGSQL consists of three key modules: (1) SQL preparation: we employ a coarse-to-fine schema linking and syntax keyword prediction, akin to how human recall and align key concepts for better understanding. (2) SQL generation: we introduce a concept-enhanced chain-of-thought prompting, enhancing NLQ interpretation and SQL composition of LLMs, similar to humans drafting SQL query. (3) SQL correction: we develop NLQ consistency and result consistency techniques to correct various errors, mirroring how humans evaluate and refine reasoning. We conduct extensive experiments using diverse benchmarks and LLMs. The results and analysis verify the effectiveness and generalizability of COGSQL. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Syntactics

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Automated Unit Test Refactoring

arXiv

引用

arXiv 2024年

作者： Gao, Yi Hu, Xing Yang, Xiaohu Xia, Xin The State Key Laboratory of Blockchain and Data Security Zhejiang University China

Test smells arise from poor design practices and insufficient domain knowledge, which can lower the quality of test code and make it harder to maintain and update. Manually refactoring of test smells is time-consuming and error-prone, highlighting the necessity for automated approaches. Current rule-based refactoring methods often struggle in scenarios not covered by predefined rules and lack the flexibility needed to handle diverse cases effectively. In this paper, we propose a novel approach called UTRefactor, a context-enhanced, LLM-based framework for automatic test refactoring in Java projects. UTRefactor extracts relevant context from test code and leverages an external knowledge base that includes test smell definitions, descriptions, and DSL-based refactoring rules. By simulating the manual refactoring process through a chain-of-thought approach, UTRefactor guides the LLM to eliminate test smells in a step-by-step process, ensuring both accuracy and consistency throughout the refactoring. Additionally, we implement a checkpoint mechanism to facilitate comprehensive refactoring, particularly when multiple smells are present. We evaluate UTRefactor on 879 tests from six open-source Java projects, reducing the number of test smells from 2,375 to 265, achieving an 89% reduction. UTRefactor outperforms direct LLM-based refactoring methods by 61.82% in smell elimination and significantly surpasses the performance of a rule-based test smell refactoring tool. Our results demonstrate the effectiveness of UTRefactor in enhancing test code quality while minimizing manual involvement. Copyright © 2024, The Authors. All rights reserved.

关键词： Automatic testing

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GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis 24

GROOT: Generating Robust Watermark for Diffusion-Model-Based...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Weizhi Li, Yue Lin, Dongdong Tian, Hui Li, Haizhou Huaqiao University Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen China

ISBN: (纸本)9798400706868

Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audiowatermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%. Our audio samples are available at https://***/. © 2024 ACM.

关键词： Audio watermarking

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Secure and Revocable Multi-authority CP-ABE for Mobile Cloud Computing 24th

Secure and Revocable Multi-authority CP-ABE for Mobile Clo...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Junyang Yan, Hongyang Koe, Arthur Sandor Voundi Deng, Weichu Zhong, Zhengxi Guangdong Key Laboratory of Blockchain Security Institute of Artificial Intelligence Guangzhou University Guangzhou China State Key Laboratory of Integrated Service Networks Xidian University Xi’an China

ISBN: (纸本)9789819615278

Mobile cloud computing (MCC) enhances mobile device capabilities with cloud-based resources, allowing mobile users to outsource more data to the cloud to enjoy services of interest. It uses ciphertext policy attribute-based encryption (CP-ABE) to ensure the security and privacy of outsourced data and to implement fine-grained access control of data. However, the existing CP-ABE schemes exhibit the key escrow problem on the central authority and fail to grant data users full control over their identity which may expose user privacy. This paper proposes a multi-authority CP-ABE scheme to solve the key escrow problem and lack of user attribute privacy in the current literature. We design a novel SSI-aware communication model and seamlessly integrate it with our multi-authority CP-ABE to protect user identity privacy and strengthen the user’s sovereignty over their identity. We show how to conduct attribute revocation in our system. We prove security under the Decisional Bilinear Diffie-Hellman assumption. This research contributes to the existing body of knowledge on secure and privacy-preserving data sharing in the mobile cloud. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning

arXiv

引用

arXiv 2025年

作者： Liu, Yuchen Chen, Chen Lyu, Lingjuan Jin, Yaochu Chen, Gang The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Sony AI Westlake University China

Federated Learning (FL) is notorious for its vulnerability to Byzantine attacks. Most current Byzantine defenses share a common inductive bias: among all the gradients, the densely distributed ones are more likely to be honest. However, such a bias is a poison to Byzantine robustness due to a newly discovered phenomenon in this paper – gradient skew. We discover that a group of densely distributed honest gradients skew away from the optimal gradient (the average of honest gradients) due to heterogeneous data. This gradient skew phenomenon allows Byzantine gradients to hide within the densely distributed skewed gradients. As a result, Byzantine defenses are confused into believing that Byzantine gradients are honest. Motivated by this observation, we propose a novel skew-aware attack called STRIKE: first, we search for the skewed gradients;then, we construct Byzantine gradients within the skewed gradients. Experiments on three benchmark datasets validate the effectiveness of our attack. © 2025, CC BY.

关键词： Federated learning

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Drift-aware Anomaly Detection for Non-stationary Time Series

Drift-aware Anomaly Detection for Non-stationary Time Series

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Gao, Yang Li, Ying Li, Yang Feng, Zunlei Song, Mingli Wang, Xingyu Chen, Chun Zhejiang University Hangzhou China The State Key Laboratory of Blockchain and Data Security Hangzhou China ZJU-Bangsun Joint Research Center Hangzhou China

ISBN: (纸本)9798350324457

Anomaly detection of time series is vital in various scenarios with explosively growing time series data. However, the non-stationary time series degrade the performance of current anomaly detection methods, where data drift causes unpredictable changes. This paper proposes a Drift-aware Anomaly Detection (DAD) method for detecting anomalies in non-stationary time series. DAD adopts a self-attention mechanism to learn an embedding, distinguishing the anomaly embeddings from the normal embeddings. Next, the KL divergence calculates the drift deviation between two data segments at adjacent periods. Then, the drift deviation module combined with the latent vector which is used to reconstruct the original vector. During the encoding stage of the time series, the latent code is modeled using different Gaussian mixture distributions and the data reconstruction error at each time tick is regarded as an anomaly metric. Furthermore, we propose a new metric to measure the degree of drift deviation for a dataset used for a fair experiment comparison. Experimental results on several public datasets and a newly collected sensor dataset demonstrate that for the non-stationary time series anomaly detection task, DAD outperforms state-of-the-art anomaly detection models up to 11.5% on the F1 score. © 2023 IEEE.

关键词： anomaly detection concept drift non-stationary time series

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Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection 38

Enhancing Semi-Supervised Learning via Representative and Di...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shao, Qian Kang, Jiangrui Chen, Qiyuan Li, Zepeng Xu, Hongxia Cao, Yiwen Liang, Jiajuan Wu, Jian College of Computer Science & Technology Liangzhu Laboratory Zhejiang University China BNU-HKBU United International College China WeDoctor Cloud China The State Key Laboratory of Blockchain and Data Security Zhejiang University China

Semi-Supervised Learning (SSL) has become a preferred paradigm in many deep learning tasks, which reduces the need for human labor. Previous studies primarily focus on effectively utilising the labelled and unlabeled data to improve performance. However, we observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. The sample selection task in SSL has been under-explored for a long time. To fill in this gap, we propose a Representative and Diverse Sample Selection approach (RDSS). By adopting a modified Frank-Wolfe algorithm to minimise a novel criterion α-Maximum Mean Discrepancy (α-MMD), RDSS samples a representative and diverse subset for annotation from the unlabeled data. We demonstrate that minimizing α-MMD enhances the generalization ability of low-budget learning. Experimental results show that RDSS consistently improves the performance of several popular SSL frameworks and outperforms the state-of-the-art sample selection approaches used in Active Learning (AL) and Semi-Supervised Active Learning (SSAL), even with constrained annotation budgets. Our code is available at RDSS. © 2024 Neural information processing systems foundation. All rights reserved.

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CDS: An Anti-Aging Calibratable Digital Sensor for Detecting Multiple Types of Fault Injection Attacks 24

CDS: An Anti-Aging Calibratable Digital Sensor for Detecting...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Chen, Zhiyuan Yang, Kun Ren, Kui The State Key Laboratory of Blockchain and Data Security Zhejiang University China School of Cyber Science and Technology Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China

ISBN: (纸本)9798400706011

Fault injection attacks (FIAs) are a class of active physical attacks that inject faults into computing devices to deliberately change their intended behaviors for malicious purposes such as security feature circumvention, privilege escalation, secret data extraction by analyzing the erroneous outputs, etc. Existing sensors that detect fault injection attacks are either susceptible to aging or suffer from complicated and costly calibration process. Worse still, most sensors will fail in the presence of dynamic voltage and frequency scaling (DVFS) because they are built upon fixed delay chains and cannot adapt to operating voltage and frequency variations. In this paper, we overcome these limitations by presenting CDS, a delay chain based digital sensor that exploits timing variations of both detector and protected object for detecting multiple types of fault injection attacks. CDS utilizes a calibration module to solve the accuracy degradation caused by aging phenomena and a dynamic adjustment module to acclimatize itself to the need of dynamically adjusting voltage according to operating frequency in the presence of DVFS. To demonstrate its capability, we use CDS to protect the hardware accelerator of PRESENT cryptographic algorithm against voltage and laser glitching attacks. Simulation results based on HSPICE show that (i) CDS can detect 100% of voltage and temperature coordinated glitching attacks with 4.1% early warning;(ii) CDS can detect 100% of laser glitching attacks with 9.1% early warning;(iii) CDS maintains outstanding aging resistance with only 1.1% false alarm rate increase after 7 years of use. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Hardware security

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