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A Multidimensional data Utility Evaluation and Pricing Scheme in the Big data Market

Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Chen, Yuling Bai, Rui Wu, Yongtang Li, Tao Zhou, Hui State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shandong Weifang262700 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Weifang Key Laboratory of Blockchain on Agricultural Vegetables Shandong Weifang262700 China

Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants' profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments. © 2023 Yuling Chen et al.

关键词： Big data

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Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model

arXiv

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arXiv 2024年

作者： Cui, Yu Liu, Feng Wang, Pengbo Wang, Bohao Tang, Heng Wan, Yi Wang, Jun Chen, Jiawei Zhejiang University Hangzhou China OPPO Research Institute Shenzhen China University of Electronic Science and Technology of China Chengdu China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the high inference latency of LLMs significantly restricts their practical deployment. To address this issue, this work investigates knowledge distillation from cumbersome LLM-based recommendation models to lightweight conventional sequential models. It encounters three challenges: 1) the teacher’s knowledge may not always be reliable;2) the capacity gap between the teacher and student makes it difficult for the student to assimilate the teacher’s knowledge;3) divergence in semantic space poses a challenge to distill the knowledge from embeddings. To tackle these challenges, this work proposes a novel distillation strategy, DLLM2Rec, specifically tailored for knowledge distillation from LLM-based recommendation models to conventional sequential models. DLLM2Rec comprises: 1) Importance-aware ranking distillation, which filters reliable and student-friendly knowledge by weighting instances according to teacher confidence and student-teacher consistency;2) Collaborative embedding distillation integrates knowledge from teacher embeddings with collaborative signals mined from the data. Extensive experiments demonstrate the effectiveness of the proposed DLLM2Rec, boosting three typical sequential models with an average improvement of 47.97%, even enabling them to surpass LLM-based recommenders in some cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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The optimal attack-defense scheme for secret sharing 15

The optimal attack-defense scheme for secret sharing

引用

2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Ma, Juan Chen, Yuling Lv, Xiao Qian, Xiaobin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin China Weifang University of Science and Technology Blockchain Laboratory of Agricultural Vegetables Weifang China Guizhou Shuanhui Big Data Industry Development Co. Ltd Guiyang China Guizhou CoVision Science &technology Co. Ltd Guiyang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

The attack-defense issue of secret sharing has been a pressing cryptography research need. For solving this issue, this study proposes an optimal secret-share attack-defense model with game theory and information theory. This study constructs an attack channel for attackers and a defense channel for defenders. Then, we propose a game model that utilizes mutual information to measure the attack and defensive capabilities of attackers and defenders. Next, this study proves that there is a Nash equilibrium in this game model by analyzing the strategies of each attacker/defender. Finally, we analyze the change of k value and attack and defensive capabilities. Moreover, we also prove the best attack and defensive strategies. © 2022 IEEE.

关键词： Game theory

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Ethereum Phishing Detection Using Hyperbolic Neural Networks and Temporal Information

Ethereum Phishing Detection Using Hyperbolic Neural Networks...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Rong Xu Jun Zhang Xiaoyue Zhu Zhaoxiong Song Zhi Yu College of Computer Science Inner Mongolia University Hohhot China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural Networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness.

关键词： Attention mechanisms Phishing Neural networks Benchmark testing Feature extraction Real-time systems blockchains Safety security Tuning

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A Multipath Source Location Privacy Protection Scheme in Wireless Sensor Networks via Proxy Node 15

A Multipath Source Location Privacy Protection Scheme in Wir...

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作者： Sun, Jing Chen, Yuling Lv, Xiao Qian, Xiaobin Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security Guilin China Weifang University of Science and Technology Blockchain Laboratory of Agricultural Vegetables Weifang China Guizhou Shuanhui Big Data Industry Development Co.Ltd Guiyang China Guizhou CoVision Science &technology Co.Ltd Guiyang China

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

Wireless sensor networks (WSNs) are widely devoted to monitoring target. Attackers can detect the source node by hop-by-hop backtracking strategy. Therefore, it is necessary to protect the location privacy of data source nodes. Source location privacy (SLP) plays a crucial role in WSNs security. To ensure the security of source location in WSNs, a multi-path source location privacy protection scheme via proxy node is proposed. Source node initiates restricted flooding and directed routing algorithm keep packets away from the source location, effectively increasing the number of random directed paths. Greedy quantitative routing is the basis of generating vast quantities of relay nodes with diverse geographical locations. The receiving node randomly generates angles to complete equal-hop routing. The sending node continuously selects any near-neighbor node as a participant until the data packet is sent to the spin node. The spin node chooses the adjacent quadrant to complete the variable length equal-hop routing through proxy nodes and forwards the packet to the sink node. Theoretical analysis and simulation experiments show that this scheme provides excellent privacy for the source location. © 2022 IEEE.

关键词： data privacy

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VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals

arXiv

引用

arXiv 2024年

作者： Singh, Alakhsimar Verma, Nischay Goyal, Kanav Singh, Amritpal Kumar, Puneet Li, Xiaobai Computer Science and Engineering Department National Institute of Technology Jalandhar India Center for Machine Vision and Signal Analysis University of Oulu Finland The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

This paper presents VisioPhysioENet, a novel multimodal system that leverages visual and physiological signals to detect learner engagement. It employs a two-level approach for extracting both visual and physiological features. For visual feature extraction, Dlib is used to detect facial landmarks, while OpenCV provides additional estimations. The face_recognition library, built on Dlib, is used to identify the facial region of interest specifically for physiological signal extraction. Physiological signals are then extracted using the plane-orthogonal-to-skin method to assess cardiovascular activity. These features are integrated using advanced machine learning classifiers, enhancing the detection of various levels of engagement. We thoroughly tested VisioPhysioENet on the DAiSEE dataset. It achieved an accuracy of 63.09%. This shows it can better identify different levels of engagement compared to many existing methods. It performed 8.6% better than the only other model that uses both physiological and visual features. Copyright © 2024, The Authors. All rights reserved.

关键词： Physiological models

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Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments

Towards Efficient Asynchronous Federated Learning in Heterog...

引用

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

作者： Yajie Zhou Xiaoyi Pang Zhibo Wang Jiahui Hu Peng Sun Kui Ren The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Cyber Science and Technology Zhejiang University Hangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated learning (FL) is widely used in edge environments as a privacy-preserving collaborative learning paradigm. However, edge devices often have heterogeneous computation capabilities and data distributions, hampering the efficiency of co-training. Existing works develop staleness-aware semi-asynchronous FL that reduces the contribution of slow devices to the global model to mitigate their negative impacts. But this makes data on slow devices unable to be fully leveraged in global model updating, exacerbating the effects of data heterogeneity. In this paper, to cope with both system and data heterogeneity, we propose a clustering and two-stage aggregation-based Efficient Asynchronous Federated Learning (EAFL) framework, which can achieve better learning performance with higher efficiency in heterogeneous edge environments. In EAFL, we first propose a gradient similarity-based dynamic clustering mechanism to cluster devices with similar system and data characteristics together dynamically during the training process. Then, we develop a novel two-stage aggregation strategy consisting of staleness-aware semi-asynchronous intra-cluster aggregation and data size-aware synchronous inter-cluster aggregation to efficiently and comprehensively aggregate training updates across heterogeneous clusters. With that, the negative impacts of slow devices and Non-IID data can be simultaneously alleviated, thus achieving efficient collaborative learning. Extensive experiments demonstrate that EAFL is superior to state-of-the-art methods.

关键词： Training Performance evaluation Adaptation models Federated learning Computational modeling Aggregates Collaboration

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Mamba Adapter: Efficient Multi-Modal Fusion for Vision-Language Tracking

引用

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Shi, Liangtao Zhong, Bineng Liang, Qihua Hu, Xiantao Mo, Zhiyi Song, Shuxiang Ministry of Education Guangxi Normal University Key Laboratory of Education Blockchain and Intelligent Technology Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Wuzhou University School of Data Science and Software Engineering Wuzhou543002 China

Utilizing the high-level semantic information of language to compensate for the limitations of vision information is a highly regarded approach in single-object tracking. However, most existing vision-language (VL) trackers employ full-parameter fine-tuning, which can easily lead to catastrophic forgetting. Therefore, they fail to fully exploit the prior knowledge of pre-trained models from upstream tasks, resulting in unsatisfactory tracking performance. To alleviate the above problem, we propose a simple yet effective Vision-Language Tracking pipeline based on Mamba Adapter, named MAVLT, which adopts the idea of parameter-efficient fine-tuning (PEFT) to realize the interaction between vision-language modalities. This novel approach offers the following advantages: (1)The knowledge of the upstream pre-trained model is efficiently inherited by freezing its parameters. This ensures that the VL tracking framework only learns the modules for vision and language interaction, with a focus on the fusion between modalities. (2)The modal interaction between language and vision encoders is flexibly bridged in each encoder layer via proposed mamba adapter, enabling efficient interaction of visual and language information at multiple levels. Extensive experiments on five popular vision-language tracking benchmarks validate the effectiveness of the proposed MAVLT. Particularly, the MAVLT achieves 73.4% AUC score on the LaSOT benchmarks with only 0.18%(0.32M) of the total parameters updates. Code and models are available at https://***/GXNU-ZhongLab/MAVLT. © 1991-2012 IEEE.

关键词： Semantics

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Decode the Dark Side of the Language: Applications of LLMs in the Dark Web

Decode the Dark Side of the Language: Applications of LLMs i...

引用

IEEE International Conference on data Science in Cyberspace (DSC)

作者： Hongfan Chen Yigang Diao Hui Xiang Yixuan Huo Xiaqing Xie Jiapeng Zhao Xuebin Wang Yanwei Sun Jinqiao Shi School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Cyberspace Administration of China Data and Technology Support Center Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Previous studies have discovered that Transformers pre-trained on large scale datasets have a great advantage over traditional methods and can achieve human-level performance in many NLP tasks. Various encoder-only models like BERT and RoBERTa are used in the researches of the language in the Dark Web and most of them have achieved state-of-the-art performance. However, decoder-only models are not frequently used as they were considered to be unsuitable for NLU tasks until recent advancements in large language models. In this paper, we discuss the application of large scale decoder-only models in NLP tasks of real-world Dark Web research. We explore the capabilities of these models with the most realistic datasets in this specific domain. Surprisingly, results show that decoder-only models can achieve similar or better performance compared with state-of-the-art encoder-only model in some cases and show their own unique advantages in such field. We also discuss the limitations of these models and the challenges associated with their application in such field. Our study unveils the potential of decoder-only models in NLP tasks of real-world Dark Web research.

关键词： Dark Web Large language models Text categorization Zero shot learning Cyberspace data science Transformers Natural language processing data models Encoding

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Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning

arXiv

引用

arXiv 2022年

作者： Liu, Shunyu Song, Jie Zhou, Yihe Yu, Na Chen, Kaixuan Feng, Zunlei Song, Mingli The State Key Laboratory of Blockchain and Security Zhejiang University Hangzhou310027 China Institute of Blockchain and Data Security Hangzhou310027 China The School of Software Technology Zhejiang University Hangzhou310027 China

Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. However, recent advances in multi-agent learning mainly focus on value decomposition while leaving entity interactions still intertwined, which easily leads to over-fitting on noisy interactions between entities. In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities. OPT facilitates filtering the noisy interactions between irrelevant entities and thus significantly improves generalizability as well as interpretability. Specifically, OPT introduces a sparse disagreement mechanism to encourage sparsity and diversity among discovered interaction prototypes. Then the model selectively restructures these prototypes into a compact interaction pattern by an aggregator with learnable weights. To alleviate the training instability issue caused by partial observability, we propose to maximize the mutual information between the aggregation weights and the history behaviors of each agent. Experiments on single-task, multi-task and zero-shot benchmarks demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://***/liushunyu/OPT. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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