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2nd International Conference on Big data and Privacy Computing, BDPC 2024

作者： Ding, Zehua Tian, Youliang Wang, Guorong Xiong, Jinbo College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China College of Computer and Cyber Security Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (纸本)9798350307597

Neural network models face two highly destructive threats in real-world applications: membership inference attacks (MIAs) and adversarial attacks (AAs). One compromises the model's confidentiality, leading to membership privacy breaches, while the other disrupts the model's availability, rendering it dysfunctional. Recent work has shown that adversarial examples can pose dual threats to neural network models, both MIAs and AAs, so that these two different types of attacks have a certain degree of intersection. However, existing defense methods typically focus on defending against one of the two attacks and cannot simultaneously address both. To address the above issues, we propose a defense method called regularization mixup adversarial training (RMAT), aiming to protect model membership privacy while ensuring model availability. The core idea of RMAT is to use a continuous augmentation strategy during training to generate mixup adversarial examples, weakening attackers' understanding of membership data, and updating the model with a new membership-weighted loss strategy to improve its generalization ability for handling unknown samples. This approach can further protect the model's membership privacy and ensure model availability. Experimental results demonstrate that compared to single defense strategies, RMAT can simultaneously protect the model's membership privacy and availability. © 2024 IEEE.

关键词： Adversarial machine learning

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Disentangled Table-Graph Representation for Interpretable Transmission Line Fault Location 39

Disentangled Table-Graph Representation for Interpretable Tr...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Na Deng, Yutong Liu, Shunyu Chen, Kaixuan Zheng, Tongya Song, Mingli State Key Laboratory of Blockchain and Data Security Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China Nanyang Technological Univerisity Singapore Big Graph Center Hangzhou City University China

ISBN: (纸本)157735897X

The fault location task in power grids is crucial for maintaining social order and ensuring public safety. However, existing methods that rely on tabular state records often neglect the intrinsic topological influences of transmission lines, resulting in a segmented approach to fault location that consists of multiple stages. In this paper, we propose an Disentangled Table-Graph representation framework, termed DTG, which integrates fault location tasks at coarse-grained line levels and fine-grained point levels within an end-to-end learning paradigm. Our innovative disentanglement strategy produces interpretable attribution coefficients that connect tabular records and transmission line topology, thereby facilitating fault location at both line- and point-levels. The joint prediction tasks designed around our disentangled tabular graph representation promote mutual information exchange between features and topology of transmission lines in an interpretable manner. Experimental results on the 7-bus system, 36-bus system and a realistic 325-bus system in China demonstrate that the proposed method adapt to different topological structures and handle different types of faults. Compared to traditional methods, DTG4Power achieves high accuracy in both fault lines and fault points. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Algebra

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The Communication-Friendly Privacy-Preserving Machine Learning against Malicious Adversaries

arXiv

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arXiv 2024年

作者： Lu, Tianpei Zhang, Bingsheng Li, Lichun Ren, Kui State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang University Hangzhou China Ant Group Hangzhou China

—With the increasing emphasis on privacy regulations, such as GDPR, protecting individual privacy and ensuring compliance have become critical concerns for both individuals and organizations. Privacy-preserving machine learning (PPML) is an innovative approach that allows for secure data analysis while safeguarding sensitive information. It enables organizations to extract valuable insights from data without compromising privacy. Secure multi-party computation (MPC) is a key tool in PPML, as it allows multiple parties to jointly compute functions without revealing their private inputs, making it essential in multi-server environments. We address the performance overhead of existing maliciously secure protocols, particularly in finite rings like 2, by introducing an efficient protocol for secure linear function evaluation. We implement our maliciously secure MPC protocol on GPUs, significantly improving its efficiency and scalability. We extend the protocol to handle linear and non-linear layers, ensuring compatibility with a wide range of machine-learning models. Finally, we comprehensively evaluate machine learning models by integrating our protocol into the workflow, enabling secure and efficient inference across simple and complex models, such as convolutional neural networks (CNNs). © 2024, CC BY.

关键词： Adversarial machine learning

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Unbiased Multi-Label Learning from Crowdsourced Annotations 41

Unbiased Multi-Label Learning from Crowdsourced Annotations

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41st International Conference on Machine Learning, ICML 2024

作者： Xia, Mingxuan Huang, Zenan Wu, Runze Lyu, Gengyu Zhao, Junbo Chen, Gang Wang, Haobo School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Fuxi AI Lab NetEase Inc. Hangzhou China Faculty of Information Technology Beijing University of Technology Beijing China

This work studies the novel Crowdsourced Multi-Label Learning (CMLL) problem, where each instance is related to multiple true labels but the model only receives unreliable labels from different annotators. Although a few Crowdsourced Multi-Label Inference (CMLI) methods have been developed, they require both the training and testing sets to be assigned crowdsourced labels and focus on true label inferring rather than prediction, making them less practical. In this paper, by excavating the generation process of crowdsourced labels, we establish the first unbiased risk estimator for CMLL based on the crowdsourced transition matrices. To facilitate transition matrix estimation, we upgrade our unbiased risk estimator by aggregating crowdsourced labels and transition matrices from all annotators while guaranteeing its theoretical characteristics. Integrating with the unbiased risk estimator, we further propose a decoupled autoencoder framework to exploit label correlations and boost performance. We also provide a generalization error bound to ensure the convergence of the empirical risk estimator. Experiments on various CMLL scenarios demonstrate the effectiveness of our proposed method. The source code is available at https://***/MingxuanXia/CLEAR. Copyright 2024 by the author(s)

关键词： Risk perception

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MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation 39

MTVHunter: Smart Contracts Vulnerability Detection Based on ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Guokai Zhuang, Yuan Zhang, Shuo Feng, Xiaoyu Liu, Zhenguang Zhang, Liguo College of Computer Science and Technology Harbin Engineering University Heilongjiang China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China

ISBN: (纸本)157735897X

Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions;(ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Teaching

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Transformer doctor: diagnosing and treating vision transformers 24

Transformer doctor: diagnosing and treating vision transform...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Hao Chen Kejia Chen Yang Gao Jingwen Ye Xingen Wang Mingli Song Zunlei Feng College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Bangsheng Technology Co. Ltd. Electrical and Computer Engineering National University of Singapore College of Computer Science and Technology Zhejiang University and Bangsheng Technology Co. Ltd. College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance. For more information, please visit https://***/.

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Exploiting Library Vulnerability via Migration Based Automating Test Generation

Exploiting Library Vulnerability via Migration Based Automat...

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International Conference on Software Engineering (ICSE)

作者： Zirui Chen Xing Hu Xin Xia Yi Gao Tongtong Xu David Lo Xiaohu Yang The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Huawei China Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

In software development, developers extensively utilize third-party libraries to avoid implementing existing functionalities. When a new third-party library vulnerability is disclosed, project maintainers need to determine whether their projects are affected by the vulnerability, which requires developers to invest substantial effort in assessment. However, existing tools face a series of issues: static analysis tools produce false alarms, dynamic analysis tools require existing tests and test generation tools have low success rates when facing complex vulnerabilities. Vulnerability exploits, as code snippets provided for reproducing vulnerabilities after disclosure, contain a wealth of vulnerability-related information. This study proposes a new method based on vulnerability exploits, called Vesta (Vulnerability Exploit-based Software Testing Auto-Generator), which provides vulnerability exploit tests as the basis for developers to decide whether to update dependencies. Vesta extends the search-based test generation methods by adding a migration step, ensuring the similarity between the generated test and the vulnerability exploit, which increases the likelihood of detecting potential library vulnerabilities in a project. We perform experiments on 30 vulnerabilities disclosed in the past five years, involving 60 vulnerability-project pairs, and compare the experimental results with the baseline method, Transfer. The success rate of Vesta is 71.7% which is a 53.4% improvement over Transfer in the effectiveness of verifying exploitable vulnerabilities.

关键词： Software testing Codes Static analysis Libraries Test pattern generators Faces Software engineering

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ℬ4: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests

ℬ4: Towards Optimal Assessment of Plausible Code Solutions ...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Mouxiang Chen Zhongxin Liu He Tao Yusu Hong David Lo Xin Xia Jianling Sun The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Zhejiang University Hangzhou China Singapore Management University Singapore Singapore

ISBN: (数字)9798400712487

ISBN: (纸本)9798331508241

Selecting the best code solution from multiple generated ones is an essential task in code generation, which can be achieved by using some reliable validators (e.g., developer-written test cases) for assistance. Since reliable test cases are not always available and can be expensive to build in practice, researchers propose to automatically generate test cases to assess code solutions. However, when both code solutions and test cases are plausible and not reliable, selecting the best solution becomes challenging. Although some heuristic strategies have been proposed to tackle this problem, they lack a strong theoretical guarantee and it is still an open question whether an optimal selection strategy exists. Our work contributes in two ways. First, we show that within a Bayesian framework, the optimal selection strategy can be defined based on the posterior probability of the observed passing states between solutions and tests. The problem of identifying the best solution is then framed as an integer programming problem. Second, we propose an efficient approach for approximating this optimal (yet uncomputable) strategy, where the approximation error is bounded by the correctness of prior knowledge. We then incorporate effective prior knowledge to tailor code generation tasks. Both theoretical and empirical studies confirm that existing heuristics are limited in selecting the best solutions with plausible test cases. Our proposed approximated optimal strategy ℬ 4 significantly surpasses existing heuristics in selecting code solutions generated by large language models (LLMs) with LLM-generated tests, achieving a relative performance improvement by up to 50% over the strongest heuristic and 246% over the random selection in the most challenging scenarios. Our code is publicly available at https://***/ZJU-CTAG/*** CONCEPTS• Computing methodologies → Artificial intelligence; • Software and its engineering → Software design engineering.

关键词： Integer programming Codes Software design Posterior probability Large language models Computational modeling Reliability theory Software Bayes methods Software engineering

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Are Human Rules Necessary? Generating Reusable APIs with CoT Reasoning and In-Context Learning

arXiv

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arXiv 2024年

作者： Mai, Yubo Gao, Zhipeng Hu, Xing Bao, Lingfeng Liu, Yu Sun, Jianling The State Key Laboratory of Blockchain and Data Security Zhejiang University China Shanghai Institute for Advanced Study of Zhejiang University China

Nowadays, more and more developers resort to Stack Overflow for solutions (e.g., code snippets) when they encounter technical problems. Although domain experts provide huge amounts of valuable solutions in Stack Overflow, these code snippets are often difficult to reuse directly. Developers have to digest the information within relevant posts and make necessary modifications, and the whole solution-seeking process can be time-consuming and tedious. To facilitate the reuse of Stack Overflow code snippets, Terragni et al. first explored transforming a code snippet in Stack Overflow into a well-formed method API (Application Program Interface) by using a rule-based approach, named APIzator. The reported performance of their approach is promising, however, after our in-depth analysis of their experiment results, we find that (1) 92.5% of APIs generated by APIzator are pointless and thus are difficult to use in practice. This is because the method name generated by APIzator (extracting verb + object) can rarely represent the method's functionality, which can hardly be claimed as meaningful/reusable APIs. (2) The authors manually summarized a number of rules to identify parameter variables and return statements for Java methods. These hand-crafted rules are extremely complex and sophisticated, and the manual rule design process is labor-intensive and error-prone. Moreover, since these rules are designed for Java, they can hardly be extended to other programming languages. Inspired by the great potential of Large Language Models (LLMs) for solving complex coding tasks, in this paper, we propose a novel approach, named Code2API, to automatically perform APIzation for Stack Overflow code snippets. Code2API does not require additional model training or any manual crafting rules and can be easily deployed on personal computers without relying on other external tools. Specifically, Code2API guides the LLMs through well-designed prompts to generate well-formed APIs for given cod

关键词： Personal computers

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Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting

arXiv

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arXiv 2024年

作者： Wang, Shiyu Chu, Zhixuan Sun, Yinbo Liu, Yu Guo, Yuliang Chen, Yang Jian, Huiyang Ma, Lintao Lu, Xingyu Zhou, Jun Ant Group Zhejiang Hangzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou China

Accurate workload forecasting is critical for efficient resource management in cloud computing systems, enabling effective scheduling and autoscaling. Despite recent advances with transformer-based forecasting models, challenges remain due to the non-stationary, nonlinear characteristics of workload time series and the long-term dependencies. In particular, inconsistent performance between long-term history and near-term forecasts hinders long-range predictions. This paper proposes a novel framework leveraging self-supervised multiscale representation learning to capture both long-term and near-term workload patterns. The long-term history is encoded through multiscale representations while the near-term observations are modeled via temporal flow fusion. These representations of different scales are fused using an attention mechanism and characterized with normalizing flows to handle non-Gaussian/non-linear distributions of time series. Extensive experiments on 9 benchmarks demonstrate superiority over existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Time series

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