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SSRN 2023年

作者： Lv, Siyi Wei, Yu Jia, Jingyu Li, Xinhao Liu, Zheli Chen, Xiaofeng Guo, Liang College of Cyber Science The College of Computer Science Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China Department of Computer Science Purdue University United States School of Cyber Engineering Xidian University Xi’an China Huawei Technology Co. Ltd Shenzhen China

The Multiparty Private Set Intersection (MPSI) protocol allows untrusted parties to compute the intersection of their sets without revealing any additional information. However, existing solutions face challenges in terms of security and efficiency. Some schemes are secure only against semi-honest adversaries and cannot effectively defend against malicious adversaries. Other schemes, while capable of withstanding malicious adversaries, exhibit efficiency problems, especially as the number of corrupt participants increases. A practical solution must maintain high security and efficiency, even when handling large-scale data and a majority of corrupted *** paper introduces a novel scheme named TH-PSI, built upon a new star-shaped topological network communication framework based on the trusted execution environment. TH-PSI has been rigorously proven to be secure within the standard UC framework against malicious adversaries. To demonstrate the efficiency of TH-PSI, we implement the TH-PSI scheme and tested it in both LAN and WAN settings, involving up to 30 participants and datasets as large as $2^{22}$. We compare TH-PSI with state-of-the-art schemes, ENOC21 and NTY21. Our experiments show that TH-PSI is an efficient solution capable of delivering results within a matter of seconds, even when dealing with data at the million-level and a majority of corrupted participants. © 2023, The Authors. All rights reserved.

关键词： Efficiency

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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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Vulnerable Region-Aware Greybox Fuzzing

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Journal of computer science & technology 2021年第5期36卷 1212-1228页

作者： Ling-Yun Situ Zhi-Qiang Zuo Le Guan Lin-Zhang Wang Xuan-Dong Li Jin Shi Peng Liu State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Information Management Nanjing UniversityNanjing 210023China Department of Computer Science University of GeorgiaAthensGA 30602U.S.A. College of Information Sciences and Technology Pennsylvania State UniversityState CollegePA 16802U.S.A

Fuzzing is known to be one of the most effective techniques to uncover security vulnerabilities of large-scale software *** fuzzing,it is crucial to distribute the fuzzing resource appropriately so as to achieve the best fuzzing performance under a limited *** distribution strategies of American Fuzzy Lop(AFL)based greybox fuzzing focus on increasing coverage blindly without considering the metrics of code regions,thus lacking the insight regarding which region is more likely to be vulnerable and deserves more fuzzing *** tackle the above drawback by proposing a vulnerable region-aware greybox fuzzing ***,we distribute more fuzzing resources towards regions that are more likely to be vulnerable based on four kinds of code *** implemented the approach as an extension to AFL named ***-scale experimental evaluations validate the effectiveness and efficiency of RegionFuzz-11 new bugs including three new CVEs are successfully uncovered by RegionFuzz.

关键词： vulnerability detection greybox fuzzing code metrics resource distribution

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 Institute of Electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

Generative AI-Enhanced Cooperative MEC of UAVs and Ground St...

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Annual Conference on information sciences and systems (CISS)

作者： Jiahao You Ziye Jia Chao Dong Qihui Wu Zhu Han The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Computer Science and Engineering Kyung Hee University Seoul South Korea

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios.

关键词： Adaptation models Uncertainty Heuristic algorithms Computational modeling Autonomous aerial vehicles Prediction algorithms Feature extraction Trajectory Vehicle dynamics Optimization

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Practitioners’ Expectations on Log Anomaly Detection

arXiv

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arXiv 2024年

作者： Ma, Xiaoxue Li, Yishu Keung, Jacky Yu, Xiao Zou, Huiqi Yang, Zhen Sarro, Federica Barr, Earl T. Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University Hong Kong Department of Computer Science City University of Hong Kong Hong Kong State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Department of Computer Science Johns Hopkins University Baltimore United States School of Computer Science and Technology Shandong University Shandong China Department of Computer Science University College London London United Kingdom

Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over the past decade, it remains unclear what are practitioners’ expectations on log anomaly detection and whether current research meets their needs. To fill this gap, we conduct an empirical study, surveying 312 practitioners from 36 countries about their expectations on log anomaly detection. In particular, we investigate various factors influencing practitioners’ willingness to adopt log anomaly detection tools. We then perform a literature review on log anomaly detection, focusing on publications in premier venues from 2014 to 2024, to compare practitioners’ needs with the current state of research. Based on this comparison, we highlight the directions for researchers to focus on to develop log anomaly detection techniques that better meet practitioners’ expectations. © 2024, CC BY-NC-SA.

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AoI-aware Incentive Mechanism for Mobile Crowdsensing using Stackelberg Game

AoI-aware Incentive Mechanism for Mobile Crowdsensing using ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mingjun Xiao Yin Xu Jinrui Zhou Jie Wu Sheng Zhang Jun Zheng School of Computer Science and Technology University of Science and Technology of China China Suzhou Institute for Advanced Research University of Science and Technology of China China Department of Computer and Information Sciences Temple Universityy State Key Laboratory for Novel Software Technology Nanjing University China

Mobile CrowdSensing (MCS) is a mobile computing paradigm, through which a platform can coordinate a crowd of workers to accomplish large-scale data collection tasks using their mobile devices. information freshness has attracted much focus on MCS research worldwide. In this paper, we investigate the incentive mechanism design in MCS systems that take the freshness of collected data and social benefits into concerns. First, we introduce the Age of information (AoI) metric to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as a novel incomplete information two-stage Stackelberg game with multiple constraints. Next, we derive the optimal strategies of this game so as to determine the optimal reward paid by the platform and the optimal data update frequency for each worker. Moreover, we prove that these optimal strategies form a unique Stackelberg equilibrium. Based on the optimal strategies, we propose an AoI-Aware Incentive (AIAI) mechanism for the MCS system, whereby the platform and all workers can maximize their utilities simultaneously. Meanwhile, the system can ensure that the AoI values of all data uploaded to the platform are not larger than a given threshold to achieve high data freshness. Extensive simulations on real-world traces are conducted to demonstrate the significant performance of AIAI.

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Hybrid Data-Free Knowledge Distillation

arXiv

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arXiv 2024年

作者： Tang, Jialiang Chen, Shuo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Center for Advanced Intelligence Project RIKEN Japan Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Data-free knowledge distillation aims to learn a compact student network from a pre-trained large teacher network without using the original training data of the teacher network. Existing collection-based and generation-based methods train student networks by collecting massive real examples and generating synthetic examples, respectively. However, they inevitably become weak in practical scenarios due to the difficulties in gathering or emulating sufficient real-world data. To solve this problem, we propose a novel method called Hybrid Data-Free Distillation (HiDFD), which leverages only a small amount of collected data as well as generates sufficient examples for training student networks. Our HiDFD comprises two primary modules, i.e., the teacher-guided generation and student distillation. The teacher-guided generation module guides a Generative Adversarial Network (GAN) by the teacher network to produce high-quality synthetic examples from very few real-world collected examples. Specifically, we design a feature integration mechanism to prevent the GAN from overfitting and facilitate the reliable representation learning from the teacher network. Meanwhile, we drive a category frequency smoothing technique via the teacher network to balance the generative training of each category. In the student distillation module, we explore a data inflation strategy to properly utilize a blend of real and synthetic data to train the student network via a classifier-sharing-based feature alignment technique. Intensive experiments across multiple benchmarks demonstrate that our HiDFD can achieve state-of-the-art performance using 120 times less collected data than existing methods. Code is available at https://***/tangjialiang97/HiDFD. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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FTG: Score-based black-box watermarking by fragile trigger generation for deep model integrity verification

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Journal of information and Intelligence 2024年第1期2卷 28-41页

作者： Heng Yin Zhaoxia Yin Zhenzhe Gao Hang Su Xinpeng Zhang Bin Luo Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui UniversityHefei 230000China Shanghai key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 200241China School of Communication and Electronic Engineering East China Normal UniversityShanghai 200241China Department of Computer Science and Technology Tsinghua UniversityBeijing 100000China School of Computer Science and Technology Fudan UniversityShanghai 200241China

Deep neural networks(DNNs)are widely used in real-world applications,thanks to their exceptional performance in image ***,their vulnerability to attacks,such as Trojan and data poison,can compromise the integrity and stability of DNN ***,it is crucial to verify the integrity of DNN models to ensure their *** research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the *** address this problem,we propose a novel score-based black-box DNN fragile watermarking framework called fragile trigger generation(FTG).The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking *** generates different fragile samples as the trigger,based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark *** prediction probability masks can promote the generation of fragile samples in corresponding distribution *** whole watermarking process does not affect the performance of the target *** verifying the watermarking information,the FTG only needs to compare the prediction results of the model on the samples with the previous *** a result,the required model parameter information is reduced,and the FTG only needs a few samples to detect slight modifications in the *** results demonstrate the effectiveness of our proposed method and show its superiority over related *** FTG framework provides a robust solution for verifying the integrity of DNN models,and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.

关键词： Neural network Sensitive sample Fragile watermarking Model integrity Fragile trigger

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Polygon search algorithm for ultra-compact multifunctional integrated photonics design

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Photonics Research 2024年第7期12卷 1527-1541页

作者： TE DU ZHENG PENG PEIXIN HUANG ZHAOJIAN ZHANG HANSI MA XINPENG JIANG JIE HUANG MINGYU LUO HONGXIN ZOU JUNBO YANG Center of Material Science College of ScienceNational University of Defense TechnologyChangsha 410073China Institute for Quantum Science and Technology College of ScienceNational University of Defense TechnologyChangsha 410073China College of Computer National University of Defense TechnologyChangsha 410073China College of Systems Engineering National University of Defense TechnologyChangsha 410073China Defense Innovation Institute Academy of Military SciencesBeijing 100071China Photonics Research Institute Department of Electrical and Electronic EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China Hunan Key Laboratory of Mechanism and Technology of Quantum Information Changsha 410073China

Ultra-compact multifunctional integrated photonic modules have great practical significance to photonic integrated circuits (PICs).However,the design effect and efficiency of the existing mainstream inverse design algorithms are incompetent when designing these *** analyze their shortcomings in this task,and propose a new,to our knowledge,inverse design algorithm named polygon search (PS) algorithm to address these *** utilize the PS algorithm to design an integrated dual-channel mode-conversion-crossing waveguide *** module integrates three functions:interconversion between TE_(0) and TE_(1),interconversion between TE_(0) and TE_(2),and channel crossing within only a 4μm×4μm footprint,and its performance is verified by experimental *** not only greatly reduces the total footprint of many PICs but also greatly improves their fabricating ***,we propose a PS-designed mode mixer and a PS-designed bending waveguide,and connect them with the integrated modules to form a four-channel crossing-mode-division-multiplexing *** system can provide multiple modes on the basis of channel crossing and transmit the output signal in the same direction in parallel within a single output waveguide,which significantly increases the communication bandwidth and decreases the footprint of *** last,we demonstrate the effect and efficiency advantages of the PS algorithm over several mainstream inverse design algorithms by a comprehensive contrast experiment and explain these advantages in theory from several perspectives.

关键词： waveguide mixer algorithm

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