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31st International Conference on Multimedia Modeling, MMM 2025

作者： Wu, Fei Zhou, Ruixuan Ji, Yimu Jing, Xiao-Yuan Nanjing University of Posts and Telecommunications Nanjing China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang China Guangdong University of Petrochemical Technology Maoming China Wuhan University Wuhan China

ISBN: (纸本)9789819620609

Fake news detection has achieved considerable progress in recent years, especially since multi-modal information was considered. However, most methods concentrate on feature-level inter-modal interaction and fusion, without considering the gap between feature-level operation and the high-level detection task. Furthermore, how to explore useful information and fully utilize both the modality-specific and cross-modal interactive features has not been well studied. In this paper, we present a novel approach named Joint Decision Network (JDN) with modality-specific and dual interactive features for multi-modal fake news detection, which includes an information-completeness-aware modality-specific feature extraction (IMFE) module, a local and global cross-modal interaction (LGCI) module, and a decision-level adaptive weighted fusion (DAWF) module. The IMFE module is designed to extract modality-specific features and preserve useful information while removing irrelevant information for classification. To explore the commonness among modalities and deal with the modality gap issue, the LGCI module performs dual inter-modal interaction. Specifically, local interaction is conducted by product between image and text representations, and global interaction is conducted by cross-modal graph propagation. The DAWF module performs weighted fusion on the predicted probabilities of the modality-specific and interactive features with weights by optimizing weighted classification loss to fully utilize the modality-specific and interactive features. Experiments on Weibo, Fakeddit, and Twitter datasets show that JDN can significantly outperform state-of-the-art related works. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Modal analysis

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Preventing the Popular Item Embedding Based Attack in Federated Recommendations 40

Preventing the Popular Item Embedding Based Attack in Federa...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Jun Li, Huan Rong, Dazhong Zhao, Yan Chen, Ke Shou, Lidan Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Aalborg University Department of Computer Science Aalborg Denmark

ISBN: (纸本)9798350317152

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense. © 2024 IEEE.

关键词： Embeddings

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PipeCo: Pipelining Cold Start of Deep Learning Inference Services on Serverless Platforms

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Proceedings of the ACM on Measurement and Analysis of Computing Systems 2025年第2期9卷 1-23页

作者： Jiaang Duan Shiyou Qian Hanwen Hu Dingyu Yang Jian Cao Guangtao Xue Shanghai Jiao Tong University Shanghai China The State Key Laboratory of Blockchain and Data Security Zhejiang University Shanghai China

The fusion of serverless computing and deep learning (DL) has led to serverless inference, offering a promising approach for developing and deploying scalable and cost-efficient deep learning inference services (DLISs). However, the challenge of cold start presents a significant obstacle for DLISs, where DL model size greatly impacts latency. Existing studies mitigate cold starts by extending keep-alive times, which unfortunately leads to decreased resource utilization efficiency. To address this issue, we introduce PipeCo, a system designed to alleviate DLIS cold start. The core concept of PipeCo is to achieve the miniaturization and pipelining of DLIS cold start. Firstly, PipeCo utilizes a vertical partitioning approach to divide each DLIS into multiple slices, prewarming slices in a sequential and overlapping manner to decrease the overall cold-start latency. Secondly, PipeCo employs an attention-based prediction mechanism to estimate periodic patterns in requests and idle containers for scheduling slices. Thirdly, PipeCo incorporates a similarity-based container matcher for the reuse of idle containers. We implemented a prototype of PipeCo on the OpenFaaS platform and conducted extensive experiments using three real-world DLIS repositories. The results demonstrate that PipeCo effectively decreases end-to-end (E2E) latency by up to 62.67% on CPU and 58.81% on GPU clusters and reduces the overall resource usage by 65.31% compared to five state-of-the-art baselines.

关键词： cold start deep learning inference service serverless computing

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Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation 27

Learning a Mini-Batch Graph Transformer via Two-Stage Intera...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Wenda Chen, Kaixuan Liu, Shunyu Zheng, Tongya Huang, Wenjie Song, Mingli State Key Laboratory of Blockchain and Security Zhejiang University China School of Software Technology Zhejiang University China Institute of Blockchain and Data Security China Big Graph Center School of Computer and Computing Science Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://***/l-wd/LGMformer. © 2024 The Authors.

关键词： Contrastive Learning

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ZHE: Efficient Zero-Knowledge Proofs for HE Evaluations

ZHE: Efficient Zero-Knowledge Proofs for HE Evaluations

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IEEE Symposium on security and Privacy

作者： Zhelei Zhou Yun Li Yuchen Wang Zhaomin Yang Bingsheng Zhang Cheng Hong Tao Wei Wenguang Chen Zhejiang University Ant Group State Key Laboratory of Blockchain and Data Security & Hangzhou High-Tech Zone Binjiang Institute of Blockchain and Data Security Hangzhou China

ISBN: (数字)9798331522360

ISBN: (纸本)9798331522377

Homomorphic Encryption (HE) allows computations on encrypted data without decryption. It can be used where the users' information are to be processed by an untrustful server, and has been a popular choice in privacy-preserving applications. However, in order to obtain meaningful results, we have to assume an honest-but-curious server, i.e., it will faithfully follow what was asked to do. If the server is malicious, there is no guarantee that the computed result is correct. The notion of verifiable HE (vHE) is introduced to detect malicious server's behaviors, but current vHE schemes are either more than four orders of magnitude slower than the underlying HE operations (Atapoor et. al, CIC 2024) or fast but incompatible with server-side private inputs (Chatel et. al, CCS 2024). In this work, we propose a vHE framework ZHE: efficient Zero-Knowledge Proofs (ZKPs) that prove the correct execution of HE evaluations while protecting the server's private inputs. More precisely, we first design two new highly-efficient ZKPs for modulo operations and (Inverse) Number Theoretic Transforms (NTTs), two of the basic operations of HE evaluations. Then we build a customized ZKP for HE evaluations, which is scalable, enjoys a fast prover time and has a non-interactive online phase. Our ZKP is applicable to all Ring-LWE based HE schemes, such as BGV and CKKS. Finally, we implement our protocols for both BGV and CKKS and conduct extensive experiments on various HE workloads. Compared to the state-of-the-art works, both of our prover time and verifier time are improved; especially, our prover cost is only roughly 27–36× more expensive than the underlying HE operations, this is two to three orders of magnitude cheaper than state-of-the-arts.

关键词： Privacy Protocols Costs Transforms Servers Cryptography Homomorphic encryption

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Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation

Improving Retrieval-Augmented Code Comment Generation by Ret...

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International Conference on Software Maintenance (ICSM)

作者： Hanzhen Lu Zhongxin Liu 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: (数字)9798350395686

ISBN: (纸本)9798350395693

Code comment generation aims to generate high-quality comments from source code automatically and has been studied for years. Recent studies proposed to integrate information retrieval techniques with neural generation models to tackle this problem, i.e., Retrieval-Augmented Comment Generation (RACG) approaches, and achieved state-of-the-art results. Generally, RACG approaches use a retriever to retrieve a code-comment pair from a retrieval base as an exemplar, combine the exemplar with the input code snippet, and feed the combined text to a generator (usually a sequence-to-sequence model) to generate the comment. However, the retrievers in previous work are built independently of their generators. This results in that the retrieved exemplars are not necessarily the most useful ones for generating comments, limiting the performance of existing approaches. To address this limitation, we propose a novel training strategy to enable the retriever to learn from the feedback of the generator and retrieve exemplars for generation. Specifically, during training, we use the retriever to retrieve the top-k exemplars and calculate their retrieval scores, and use the generator to calculate a generation loss for the sample based on each exemplar. By aligning high-score exemplars retrieved by the retriever with low-loss exemplars observed by the generator, the retriever can learn to retrieve exemplars that can best improve the quality of the generated comments. Based on this strategy, we propose a novel RACG approach named Jointcom and evaluate it on two real-world datasets, JCSD and PCSD. The experimental results demonstrate that our approach surpasses the state-of-the-art baselines by 7.3% to 30.0% in terms of five metrics on the two datasets. We also conduct a human evaluation to compare Joint Com with the best-performing baselines. The results indicate that Jointcomoutperforms the baselines, producing comments that are more natural, informative, and useful.

关键词： Training Measurement Software maintenance Java Codes Limiting Source coding Information retrieval Generators Python

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PS3: Precise Patch Presence Test Based on Semantic Symbolic Signature 24

PS3: Precise Patch Presence Test Based on Semantic Symbolic ...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Zhan, Qi Hu, Xing Li, Zhiyang Xia, Xin Lo, David Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei China Singapore Management University Singapore

ISBN: (纸本)9798400702174

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named PS3, referring to precise patch presence test based on semantic-level symbolic signature. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. Then PS3 can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that PS3 achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. PS3 outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options. © 2024 ACM.

关键词： Software design

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DECRL: a deep evolutionary clustering jointed temporal knowledge graph representation learning approach 24

DECRL: a deep evolutionary clustering jointed temporal knowl...

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

作者： Qian Chen Ling Chen State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University

ISBN: (纸本)9798331314385

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a Deep Evolutionary Clustering jointed temporal knowledge graph Representation Learning approach (DECRL). Specifically, a deep evolutionary clustering module is proposed to capture the temporal evolution of high-order correlations among entities. Furthermore, a cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters across timestamps, thereby maintaining the temporal smoothness of clusters. In addition, an implicit correlation encoder is introduced to capture latent correlations between any pair of clusters under the guidance of a global graph. Extensive experiments on seven real-world datasets demonstrate that DECRL achieves the state-of-the-art performances, outperforming the best baseline by an average of 9.53%, 12.98%, 10.42%, and 14.68% in MRR, Hits@1, Hits@3, and Hits@10, respectively.

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LAPAA: A Secure and Lightweight Authentication Protocol for Edge Computing Built on ASCON Algorithm 6

LAPAA: A Secure and Lightweight Authentication Protocol for ...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Zhang, Linghui Zhou, Changli Wen, Jingliang HuaQiao University College of Computer Science and Technology Xiamen361021 China HuaQiao University Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China

ISBN: (纸本)9798350388374

Facing the challenges of next-generation network security, Mobile Edge Computing (MEC) serves as a decentralized model designed to meet the growing demands for data processing and reduce latency, making its security increasingly critical. However, with the rapid development of edge computing technology, traditional authentication protocols have become inadequate due to their complexity, insufficient protection of user anonymity, and neglect of resource constraints. These issues are at the core of current network security challenges. In response, this paper innovatively proposes LAPAA - a secure and lightweight authentication protocol for Edge computing built on ASCON algorithm. LAPAA effectively addresses the problems found in previous solutions and protects user privacy through anonymity by combining the symmetric encryption algorithm ASCON with asymmetric elliptic curve cryptography (ECC). It employs clever parameter and computation strategies to maintain key consistency between edge devices and MEC servers, while avoiding the leakage of users' real identities. Furthermore, due to the lightweight nature of the ASCON algorithm itself, LAPAA provides superior low-latency performance for the protocol. Additionally, through simulation verification and informal security analysis using the Scyther tool, LAPAA demonstrates its strong defense against various malicious threats and comparative evaluations with other protocols highlight LAPAA's significant advantages in various aspects. Collectively, this research offers a secure and streamlined authentication strategy for MEC contexts, underscoring LAPAA's inventive and groundbreaking approach to overcoming prevalent obstacles. © 2024 IEEE.

关键词： Authentication Protocol

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LeapGNN: accelerating distributed GNN training leveraging feature-centric model migration 25

LeapGNN: accelerating distributed GNN training leveraging fe...

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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Xuechen Zhang The State Key Laboratory of Blockchain and Data Security Zhejiang University and Zhejiang Lab and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver

ISBN: (纸本)9781939133458

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that leverages a refined structure to enhance locality, combined with the model migration technique, to minimize remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2× compared to the state-of-the-art method, namely P3.

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