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2020 European Conference on Pattern Languages of Programs, EuroPLoP 2020

作者： Muñoz, Antonio Fernandez, Eduardo B. Network Information and Computer Security Lab University of Malaga Malaga Spain Dept. of Computer and Electrical Eng. and Computer Science Florida Atlantic University Boca RatonFL United States

ISBN: (纸本)9781450377690

Trusted Computing (TC) is a valuable technology to use when we need to process highly sensitive data and several variants of it are now available. We present here a pattern that describes the architecture of a standard for TC, the Trusted Platform Module (TPM), which is an architecture for which there are several possible implementations. Standards are effectively patterns because of the freedom to implement them in a variety of ways. The TPM architecture contains cryptographic functions to attest the use of legitimate hardware and software platforms and defines operations to perform related trusted operations. Some of its variants are intended to only store private keys in a secure way. Although promising, this technology often has not been properly used. This motivated us for defining a comprehensive methodology that facilitates the design and development of secure TC-equipped systems that can fully exploit the functionalities that this technology provides. This paper provides the first step of this methodology in the form of a security pattern. We have tried to provide a reasonable amount of information in our pattern description, aimed at the needs of application designers. This pattern is described in an abstract form independent of implementation details, but expressed with enough detail and precision for the use of designers. © 2020 ACM.

关键词： Trusted computing

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BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

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International Conference on computer Supported Cooperative Work in Design

作者： Xianxian Li Jie Li Feng Yu Xuemei Fu JunHao Yang Yue Chen Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781728165974;9781728165981

Encrypted image retrieval may return incorrect or incomplete results due to threats from malicious cloud servers. Most of the existing solutions focus on the efficiency and accuracy of retrieval, lack of verification of the completeness of search results, to achieve the reliability of search results and the transparency of the search process, we explore characteristic such as the decentralization and tamper-proof of blockchain, proposed a blockchain-based encrypted image retrieval scheme. This scheme stores the encrypted index on the blockchain (Ethereum), through the blockchain consensus mechanism and the function of searching on the smart contract, ensures the integrity and correctness of search results, then outsources the corresponding encrypted images to the cloud server to reduce storage cost, and designs a double-layer index structure using the bag of visual word model and simhash in the process of image similarity index. Experiments show that the reliability, high retrieval efficiency, and precision of the scheme also have a good privacy protection effect.

关键词： Privacy Visualization Image retrieval Smart contracts Blockchain Search problems Encryption

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A Private Statistic Query Scheme for Encrypted Electronic Medical Record System *

A Private Statistic Query Scheme for Encrypted Electronic Me...

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International Conference on computer Supported Cooperative Work in Design

作者： Xianxian Li Xuemei Fu Feng Yu Zhenkui Shi Jie Li Junhao Yang Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

In this paper, we propose a scheme that supports statistic query and authorized access control on an Encrypted Electronic Medical Records Databases(EMDB). Different from other schemes, it is based on Differential-Privacy(DP), which can protect the privacy of patients. By deploying an improved Multi-Authority Attribute-Based Encryption(MA-ABE) scheme, all authorities can distribute their search capability to clients under different authorities without additional negotiations. To our best knowledge, there are few studies on statistical queries on encrypted data. In this work, we consider that support differentially-private statistical queries. To improve search efficiency, we leverage the Bloom Filter(BF) to judge whether the keywords queried by users exists. Finally, we use experiments to verify and evaluate the feasibility of our proposed scheme.

关键词： Access control Data privacy Privacy Histograms Databases Conferences Cryptography

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Across-Platform Detection of Malicious Cryptocurrency Transactions via Account Interaction Learning

arXiv

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arXiv 2024年

作者： Che, Zheng Shen, Meng Tan, Zhehui Du, Hanbiao Zhu, Liehuang Wang, Wei Chen, Ting Zhao, Qinglin Xie, Yong The School of Computer Science Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering Macau University of Science and Technology China The School of Computer and Information Science Qinghai Institute of Technology Xining810016 China

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious transaction detection methods rely on large amounts of labeled data and suffer from low generalization. label-efficient and generalizable malicious transaction detection remains a challenging task. In this paper, we propose ShadowEyes, a novel malicious transaction detection method. Specifically, We first propose a generalized graph structure named TxGraph as a representation of malicious transaction, which captures the interaction features of each malicious account and its neighbors. Then we carefully design a data augmentation method tailored to simulate the evolution of malicious transactions to generate positive pairs. To alleviate account label scarcity, we further design a graph contrastive mechanism, which enables ShadowEyes to learn discriminative features effectively from unlabeled data, thereby enhancing its detection capabilities in real-world scenarios. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the zero-shot learning scenario, it can achieve an F1 score of 76.98% for identifying gambling transactions, surpassing the SOTA method by 12.05%. In the scenario of across-platform malicious transaction detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. Copyright © 2024, The Authors. All rights reserved.

关键词： Decentralized finance

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Trajectory Privacy Protection with Pricing Awareness on Ride-on-Demand System

Trajectory Privacy Protection with Pricing Awareness on Ride...

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IEEE International Conference on Cyber security and Cloud Computing (CSCloud)

作者： Sihui Jia Saiqin Long Zhirun Zheng Qingyong Deng Ping Wang Shujuan Tian School of Computer Science and School of Cyberspace Science Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan University Xiangtan China School of Information Science and Technology Jinan University Guangzhou China School of Mathematics and Compulational Science Xiangtan University Xiangtan China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

With the widespread use of the Ride-on Demand (RoD) system, many privacy issues have been exposed, and there is growing concern about whether private information will be leaked. For this problem, our previous work addressed the issue of the user’s initial and final location leakage and provided a strong utility guarantee in the RoD system. Further, the trajectory information is also important in the RoD system, it could contain a lot of private information about the user, such as health or identity, so it’s important to publish a distorted and productive trajectory. For this purpose, in this paper, we provide our trajectory protection method with pricing awareness based on previous work; the method uses supply and demand density function to guide the division of a discrete spatial grid, then uses the Markov chain to generate distorted trajectories on the grid to ensure trajectory continuity, and makes corresponding defenses against several attacks, such as Bayesian. The experiment results on real-world datasets prove the validity and robustness of the method.

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A Lightening Fine-grained Access Control Scheme Supporting Full Policy Hiding in Cloud Storage

A Lightening Fine-grained Access Control Scheme Supporting F...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jiaoli Shi Chao Hu Shimao Yao Zhuolin Mei Bin Wu Hui Li School of Computer and Big Data Jiujiang Univ. Jiujiang China School of Software IoT Engineering Jiangxi Univ. of Finance and Economics Nanchang China Jiujiang Key Lab. of Network and Information Security Jiujiang China School of Computer and Big Data Jiujiang University Jiujiang China School of Elec. and Comp. Engineering Peking University Shenzhen China

Efficiency, security, and flexibility are difficult to balance in a full policy hiding CP-ABE scheme. This paper creatively converts the privacy matching problem of access policy and a user’s private key to the privacy three-party set computation problem. Then the new privacy set computation problem is resolved by a message driven key generation method. Additionally, the ROBDD (Reduced Ordered Binary Decision Diagrams) is utilized to rich access structure expression and convert access policies into feasible path sets. Each feasible path is composed of attributes and these attributes are bound to the ciphertext using CP-ABE. Based on the innovative ideas mentioned above, we present a Lighten Fine-grained Access Control scheme supporting Full Policy Hidden (LFAC-FPH) that integrates data confidentiality protection, access policy rich expression but complete hiding, fine-grained access control, and lightweight terminal computing. Our scheme has three advantages: 1) High Efficiency. The proposed scheme can achieve fast privacy matching of access policies during user decryption. 2) Full Policy Hiding. The access policy bound by an owner on the ciphertext does not leak any information. Moreover, a user cannot know attributes they do not own. 3) High Flexibility. The decryption process does not require online from owners or KGC, and system attributes can be added at any time.

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FAST: a dual-tier few-shot learning paradigm for whole slide image classification 24

FAST: a dual-tier few-shot learning paradigm for whole slide...

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

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

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HSIGene: A Foundation Model for Hyperspectral Image Generation

arXiv

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arXiv 2024年

作者： Pang, Li Cao, Xiangyong Tang, Datao Xu, Shuang Bai, Xueru Zhou, Feng Meng, Deyu The School of Computer Science and Technology The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi’an710021 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China The Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China Xidian University Xi’an710071 China The School of Mathematics and Statistics The Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring. However, due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks. Although some recent studies have attempted to employ diffusion models to synthesize HSIs, they still struggle with the scarcity of HSIs, affecting the reliability and diversity of the generated images. Some studies propose to incorporate multi-modal data to enhance spatial diversity, but spectral fidelity cannot be ensured. In addition, existing HSI synthesis models are typically uncontrollable or only support single-condition control, limiting their ability to generate accurate and reliable HSIs. To alleviate these issues, we propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control, allowing for more precise and reliable HSI generation. To enhance the spatial diversity of the training data while preserving spectral fidelity, we propose a new data augmentation method based on spatial super-resolution, in which HSIs are upscaled first, and thus abundant training patches could be obtained by cropping the high-resolution HSIs. In addition, to improve the perceptual quality of the augmented data, we introduce a novel two-stage HSI super-resolution framework, which first applies RGB bands super-resolution and then utilizes our proposed Rectangular Guided Attention network (RGAN) for guided HSI super-resolution. Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution. The code and models are available at https://***/LiPang/HSIGene. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Semantic and Emotional Dual Channel for Emotion Recognition in Conversation

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IEEE Transactions on Affective Computing 2025年

作者： Yang, Zhenyu Zhang, Zhibo Cheng, Yuhu Zhang, Tong Wang, Xuesong Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China China University of Mining and Technology Engineering Research Center of Intelligent Control for Underground Space Ministry of Education Xuzhou Key Laboratory of Artificial Intelligence and Big Data School of Information and Control Engineering Xuzhou221116 China South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue;the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://***/r/SEDC-FCF1. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation

arXiv

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arXiv 2022年

作者： Peng, Jiangjun Wang, Hailin Cao, Xiangyong Liu, Xinlin Rui, Xiangyu Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaan’xi Xi'An710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi'An710049 China The School of Mathematics and Statistics Southwest University Chongqing400715 China School of Mathematics and Information China West Normal University Nanchong637002 China

Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based methods. The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data X ∈ RMN×B. For the data-based methods, they perform very fast on new test data once they have been trained. However, their generalization ability is always insufficient. In this paper, we propose a fast model-based HSI denoising approach. Specifically, we propose a novel regularizer named Representative Coefficient Total Variation (RCTV) to simultaneously characterize the low rank and local smooth properties. The RCTV regularizer is proposed based on the observation that the representative coefficient matrix U ∈ RMN×R(R Copyright © 2022, The Authors. All rights reserved.

关键词： Image denoising

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