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BTC-Shadow: an analysis and visualization system for exposing implicit behaviors in Bitcoin transaction graphs

Frontiers of computer science 2023年第6期17卷 167-169页

作者： Ding BAO Wei REN Yuexin XIANG Weimao LIU Tianqing ZHU Yi REN Kim-Kwang Raymond CHOO School of Computer Science China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan 430074China NSFOCUS Technologies Group Co. Ltd.Beijing 100089China School of Computing Sciences University of East AngliaNorwich NR47TJUK Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

1 Introduction A Bitcoin ledger comprises a sizable number of transaction records,which can be utilized to make it easier to track and analyze the traits and patterns of cryptocurrency-related *** facilitate the visual analysis of Bitcoin,numerous tools with various aims have been *** example,MiningVis[1]and SuPoolVisor[2]are the analytics systems for Bitcoin mining pools,as well as[3-5]focus on the Bitcoin transaction graphs ***,due to our previous research requirements for Bitcoin transaction graphs,none of the available tools can provide exploring the features of connection related to the address and observe significant visual ***,using these tools is challenging to navigate to abnormal node clusters effortlessly from large node groups and then analyze the local interlink characteristics between transaction nodes with interactive analytics.

关键词： analytic implicit utilized

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Privacy-Preserving Interactive Semantic Codec Training for IoT-Based Holographic Counterparts

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IEEE Transactions on Consumer Electronics 2025年

作者： Xu, Jinpeng Chen, Liang Lin, Limei Wang, Xiaoding Huang, Yanze Xu, Li Piran, Md. Jalil Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

The use of IoT-based semantic codecs to process complex contextual semantic information in holographic counterparts introduces significant privacy risks, as it may expose sensitive data, thereby increasing the likelihood of privacy disclosures. The diverse and dynamic nature of holographic counterparts in IoT environments exacerbates these challenges, making it more difficult for semantic codecs to effectively safeguard data privacy. This complexity further intensifies the need for privacy-preserving computation methods, as ensuring the confidentiality and security of the data processed by these codecs becomes a critical concern. However, current privacy protection strategy for multi-party training of semantic codecs relies heavily on the central server for gradient calculation, which may lead to gradient leakage issue. To address this issue, we propose PIMSeC (Privacy-Preserving Interactive Multi-Party Semantic Codec Training for IoT-Based Holographic Counterparts), a novel encryption-based technique that facilitates secure and efficient multi-party interactive training without the dependence on the central server, which enhances both data security and privacy resilience. PIMSeC not only proposes a full interactive secure multi-party deep learning model to protect data privacy during multi-party interactive training, but also, within the above deep learning model, establishes an encrypted additive gradient noise mechanism to ensure post-training semantic codec data privacy. Our theoretical analysis and experimental results demonstrate that PIMSeC promotes semantic codecs privacy protection effectively by interactive secure multi-party training. Compared to the state-of-art methods, PIMSeC achieves a 3% to 15% improvement in terms of accuracy, precision, F1-score, and recall at lower compression rates. © 1975-2011 IEEE.

关键词： Deep learning

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TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction 5th

TGNN: A GNN-Based Method with Multi-entity Node for Person...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Mo, Zhikang Shao, Qixiang Wu, Likang Yu, Runlong Liu, Qi Xu, Jiexin Song, Hongmei Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Computer Science and Techonology University of Science and Technology of China Hefei China Department of Information Technology China Merchant Bank Shenzhen China

ISBN: (纸本)9789811989148

The offline business of banks is deeply involved in bank clients’ financial lives and directly affects the satisfaction of clients. For offline banking scenarios, accurate prediction of clients’ banking time is essential for the service system of the banks, which can be used for multiple downstream tasks, such as queuing time prediction, real-time queuing planning, etc. However, the problem of banking time prediction remains open due to many technical and domain challenges, such as Anonymous Client Problem (ACP) and Sparse Interaction Problem (SIP). The existing works mainly utilize the traditional machine learning method and are relatively plain in modeling this practical scenario thus leading to an unpromising performance. Different from existing works, we predict the personal banking time by modeling the clients and banking services in the banking process through mining a large number of banking records. In this paper, we propose a Tuple-node-based Graph Neural Network (TGNN) framework to learn representations of clients and services of banks and estimate the personal banking time based on the representations. Empirical studies on two real-world datasets collected from a fintech bank demonstrate the effectiveness of TGNN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Graph Sampling based Fairness-aware Recommendation over Sensitive Attribute Removal

Graph Sampling based Fairness-aware Recommendation over Sens...

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IEEE International Conference on Data Mining (ICDM)

作者： Shenghao Liu Guoyang Wu Xianjun Deng Hongwei Lu Bang Wang Laurence Yang James J. Park Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Smart Internet Technology School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea

Discrimination against different user groups has received growing attention in the recommendation field. To address this problem, existing works typically remove sensitive attributes that may cause discrimination through adversary learning to achieve fair recommendations. However, these approaches leverage all available interactions for learning user representations and overlook the fact that different interactions have varying relevance to users’ sensitive attributes. Ignoring this issue may weaken the effectiveness of adversary learning in removing sensitive attributes. To tackle this challenge, we propose a novel model called GS-FairRec, which distinguishes between user interactions to achieve better removal of sensitive attributes. The model consists of three modules: graph sampling-based representation learning, pseudo-user representation learning, and adversarial learning. Firstly, the graph sampling-based representation learning module removes some irrelevant neighbors from a user-item bipartite graph and employs a graph convolutional network (GCN) to learn user/item representations. Next, items that are relevant to a user’s sensitive information but do not match their preferences are defined as the user’s pseudo-interest items, which are leveraged to learn the pseudo-user representation. In the adversarial learning module, the user’s two kinds of representations are fused for adversarial learning to remove sensitive information. Additionally, we design a new metric to measure the model’s ability to remove sensitive attributes based on how a generated recommendation list discloses the user’s sensitive attributes. Finally, we conduct experiments on two real-world datasets, and our results demonstrate the superiority of our proposed model in fairness tasks.

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Industrial Control Protocol Type Inference Using Transformer and Rule-based Re-Clustering

Industrial Control Protocol Type Inference Using Transformer...

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

作者： Yuhuan Liu Yulong Ding Jie Jiang Bin Xiao Shuang-Hua Yang Department of Computing The Hong Kong Polytechnic University Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet College of Information Science and Engineering China University of Petroleum-Beijing Department of Computer Science University of Reading

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The development of the Industrial Internet of Things (IIoT) is impeded by the lack of unknown protocol specifications. Protocol Reverse Engineering (PRE) plays a crucial role in inferring unpublished protocol specifications by analyzing traffic messages. Since different types within a protocol often have distinct formats, inferring the protocol type is essential for subsequent reverse analysis. Natural Language Processing (NLP) models have demonstrated remarkable capabilities in various sequence tasks, and traffic messages of unknown protocols can be analyzed as sequences. In this paper, we propose a framework for clustering unknown industrial control protocol types. Our framework utilizes a transformer-based auto-encoder network to train corresponding request and response messages, leveraging intermediate layer embedding vectors learned by the network for clustering. The clustering results are employed to extract candidate keywords and establish empirical rules. Subsequently, rule-based re-clustering is performed, and its effectiveness is evaluated based on previous clustering results. Through this re-clustering process, we identify the most effective combination of keywords that define the type. We evaluate the proposed framework using three general protocols that have different type rules and successfully separate the protocol internal types completely.

关键词： Protocols Automation Industrial control Reverse engineering Transformers Natural language processing Vectors

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TrafPS: A Shapley-based Visual Analytics Approach to Interpret Traffic

arXiv

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arXiv 2024年

作者： Feng, Zezheng Jiang, Yifan Wang, Hongjun Fan, Zipei Ma, Yuxin Yang, Shuang-Hua Qu, Huamin Song, Xuan Department of Computer Science and Engineering Southern University of Science and Technology China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer Science University of Reading United Kingdom Center for Spatial Information Science The University of Tokyo Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and Technology China

Recent achievements in deep learning (DL) have shown its potential for predicting traffic flows. Such predictions are beneficial for understanding the situation and making decisions in traffic control. However, most state-of-the-art DL models are considered "black boxes" with little to no transparency for end users with respect to the underlying mechanisms. Some previous work tried to "open the black boxes" and increase the interpretability of how predictions are generated. However, it still remains challenging to handle complex models on large-scale spatio-temporal data and discover salient spatial and temporal patterns that significantly influence traffic flows. To overcome the challenges, we present TrafPS, a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban planning. The measurements, region SHAP and trajectory SHAP, are proposed to quantify the impact of flow patterns on urban traffic at different levels. Based on the task requirement from the domain experts, we employ an interactive visual interface for multi-aspect exploration and analysis of significant flow patterns. Two real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and decision-making support for urban planning. Copyright © 2024, The Authors. All rights reserved.

关键词： Data visualization

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Evolution of AVS video coding standards: twenty years of innovation and development

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science China(information sciences) 2022年第9期65卷 50-73页

作者： Siwei MA Li ZHANG Shiqi WANG Chuanmin JIA Shanshe WANG Tiejun HUANG Feng WU Wen GAO Institute of Digital Media School of Computer Science Peking University Bytedance Inc. Department of Computer Science City University of Hong Kong CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China

Ever since the founding of the Audio and Video Coding Standard(AVS) Workgroup of China in 2002, it has been dedicated to advancing and innovating the digital audio-video industry with highly efficient and economical encoding/decoding technologies. Three representative generations of video coding standards have been finalized and published, consistently improving the coding performance in the past two *** series of AVS standards establish solid foundations for ubiquitous video applications in the areas including acquisition, coding, production, delivery, integrated system, public service, general screen content, and mixed reality media. Along with the standardization process, an extensive amount of studies have been carried out on efficiency-aware designation, algorithm optimization, and hardware implementation of these innovative video coding techniques. This paper explains how those developed techniques provide a lasting impact on the video coding community, extensively, technologically, and systematically. In particular, we provide a comprehensive survey of the three generations of the standards, and timely and in-depth summarize the efforts of the AVS video coding standards in the twenty years. The rate-distortion performance comparisons,in particular in terms of the 8K ultra-high-definition(UHD) contents, reflect the elegant design of the state-of-the-art AVS3 standards. We have also elaborated on a variety of well-established and promising applications, including commercial level real-time 8K encoder, high-frame-rate decoder chip for cell phone,and live streaming solution for sports. In addition, the China Central Television(CCTV) of China Media Group(CMG), the state television of China, has officially launched the first 8K broadcasting channel(CCTV-8K) since 2021 using AVS3. Given the significant success realized by the AVS standards, it is envisioned that a new era of 8K UHD video is arriving.

关键词： video coding AVS standards video applications intelligent coding

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Tissue P systems with Look-Ahead Mode

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Chinese Journal of Electronics 2025年第1期23卷 81-86页

作者： Yun Jiang Tao Song Zheng Zhang School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Department of Control Science and Engineering Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China

Tissue P systems are a class of distributed and parallel computing models inspired from inter-cellular communication and cooperation between cells. In this work, a variant of tissue P system, named tissue P system with look-ahead mode, is discussed for decreasing the inherent non-determinism of tissue P systems and helping implementing tissue P systems on computers. Such systems are proved to be universal by simulating register machine, and they are also proved to be able to efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with a polynomial solution to 3-coloring problem.

关键词： computers Computational modeling Medical services Parallel processing Polynomials Computational efficiency Registers Business

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A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack Against Split Learning

A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xiaoyang Xu Mengda Yang Wenzhe Yi Ziang Li Juan Wang Hongxin Hu Yong Zhuang Yaxin Liu Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Department of Computer Science and Engineering University at Buffalo SUNY

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works, FORA relies on limited prior knowledge, specifically that the server utilizes auxiliary samples from the public without knowing any client's private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning, aiming to closely mimic the victim client's representation preference. Leveraging this substitute client, the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA's superior performance compared to state-of-the-art methods. Furthermore, the paper systematically evaluates the proposed method's applicability across diverse settings and advanced defense strategies.

关键词： computer vision computer aided instruction Distance learning Transfer learning Training data Privacy breach Feature extraction

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Tensor Dynamic Fusion Based Modality-Imbalanced Multimodal Federated Learning in Mobile Edge Computing for Consumer Applications

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IEEE Transactions on Consumer Electronics 2024年

作者： Nie, Xin Yi, Lingzhi Yang, Laurence T. Deng, Xianjun Fan, Fulan Zhang, Hongtao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei EngineeringResearch Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China St. Francis Xavier University Department of Computer Science Antigonish Canada South-Central Minzu University School of Education Wuhan430074 China

Mobile edge computing extends the capabilities of computation and storage from the cloud to the edge, enabling local data storage and processing. This computing paradigm complements federated learning (FL), as it satisfies the need for privacy protection while providing efficiency and quick response. In consumer applications, multimodal data is experiencing explosive growth. Therefore, multimodal FL has emerged as a crucial research area. However, applying traditional FL methods directly to multimodal data poses certain challenges. Additionally, issues like sensor failures result in modality-imbalanced data among clients, i.e., the number and type of data modalities between client devices vary. Hence, it is imperative to design specialized FL frameworks and models specifically tailored for analyzing multimodal data. To address these challenges, we propose a tensor fusion-based multimodal federated learning (TenMFL). First, a tensor dynamic fusion network is designed for addressing the challenge of multimodal data fusion. This approach separates the parameter forward computation and the modal fusion computation, effectively resolving issues related to client-side modality imbalance. Second, we introduce a hybrid fusion method that combines feature fusion and decision fusion to further exploit the complementarity of multimodal data. We evaluate TenMFL on three multimodal datasets, experimental results demonstrate our superior performances. © 1975-2011 IEEE.

关键词： Federated learning

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