Query：
学者姓名：刘西蒙
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
In cloud-based health monitoring services, healthcare centers often outsource support vector machine (SVM)-based clinical decision models to provide remote users with clinical decisions. During service provisioning, authorized external organizations like insurance companies aim to verify decision correctness to prevent fraudulent medical reimbursements. However, existing verifiable and secure SVM classification schemes have predominantly focused on user self-verification, thereby introducing potential risks of privacy leakage (such as input data exposure) in publicly verifiable scenarios. To address the aforementioned limitation, we propose a publicly verifiable and secure SVM classification scheme (PVSSVM) for cloud-based health monitoring services in a malicious setting, which can accommodate the verification needs of users or authorized external organizations with respect to potential malicious results returned by cloud servers. Specifically, we utilize homomorphic encryption and secret sharing to protect the model and data confidentiality in the cloud server, respectively. Based on a multiserver verifiable computation framework, PVSSVM achieves public verification of predicted results. Additionally, we further investigate its performance. Experimental evaluations demonstrate that PVSSVM outperforms existing state-of-the-art solutions in terms of computation and communication overhead. Notably, in the verification scenario of large-scale predictions, the proposed scheme achieves a reduction of approximately 83.71% in computation overhead through batch verification, as compared to one-by-one verification.
Keyword ：
Cloud computing Cloud computing public verification public verification remote health monitoring services remote health monitoring services secure support vector machine (SVM) classification secure support vector machine (SVM) classification
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lei, Dian , Liang, Jinwen , Zhang, Chuan et al. Publicly Verifiable and Secure SVM Classification for Cloud-Based Health Monitoring Services [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) : 9829-9842 . |
| MLA | Lei, Dian et al. "Publicly Verifiable and Secure SVM Classification for Cloud-Based Health Monitoring Services" . | IEEE INTERNET OF THINGS JOURNAL 11 . 6 (2024) : 9829-9842 . |
| APA | Lei, Dian , Liang, Jinwen , Zhang, Chuan , Liu, Ximeng , He, Daojing , Zhu, Liehuang et al. Publicly Verifiable and Secure SVM Classification for Cloud-Based Health Monitoring Services . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (6) , 9829-9842 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Miao, Yinbin , Kuang, Da , Li, Xinghua et al. Efficient privacy-preserving federated learning under dishonest-majority setting [J]. | SCIENCE CHINA-INFORMATION SCIENCES , 2024 , 67 (5) . |
| MLA | Miao, Yinbin et al. "Efficient privacy-preserving federated learning under dishonest-majority setting" . | SCIENCE CHINA-INFORMATION SCIENCES 67 . 5 (2024) . |
| APA | Miao, Yinbin , Kuang, Da , Li, Xinghua , Leng, Tao , Liu, Ximeng , Ma, Jianfeng . Efficient privacy-preserving federated learning under dishonest-majority setting . | SCIENCE CHINA-INFORMATION SCIENCES , 2024 , 67 (5) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Due to enormous computing and storage overhead for well-trained Deep Neural Network (DNN) models, protecting the intellectual property of model owners is a pressing need. As the commercialization of deep models is becoming increasingly popular, the pre-trained models delivered to users may suffer from being illegally copied, redistributed, or abused. In this paper, we propose DeepDIST, the first end-to-end secure DNNs distribution framework in a black-box scenario. Specifically, our framework adopts a dual-level fingerprint (FP) mechanism to provide reliable ownership verification, and proposes two equivalent transformations that can resist collusion attacks, plus a newly designed similarity loss term to improve the security of the transformations. Unlike the existing passive defense schemes that detect colluding participants, we introduce an active defense strategy, namely damaging the performance of the model after the malicious collusion. The extensive experimental results show that DeepDIST can maintain the accuracy of the host DNN after embedding fingerprint conducted for true traitor tracing, and is robust against several popular model modifications. Furthermore, the anti-collusion effect is evaluated on two typical classification tasks (10-class and 100-class), and the proposed DeepDIST can drop the prediction accuracy of the collusion model to 10% and 1% (random guess), respectively.
Keyword ：
anti-collusion anti-collusion Deep neural networks Deep neural networks digital fingerprinting digital fingerprinting digital watermarking digital watermarking
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Cheng, Hang , Li, Xibin , Wang, Huaxiong et al. DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (1) : 97-109 . |
| MLA | Cheng, Hang et al. "DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34 . 1 (2024) : 97-109 . |
| APA | Cheng, Hang , Li, Xibin , Wang, Huaxiong , Zhang, Xinpeng , Liu, Ximeng , Wang, Meiqing et al. DeepDIST: A Black-Box Anti-Collusion Framework for Secure Distribution of Deep Models . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2024 , 34 (1) , 97-109 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Outsourcing storage has emerged as an effective solution to manage the increasing volume of data. With the popularity of pay-as-you-go payment models in outsourcing storage, data auditing schemes that prioritize timeliness can be valuable evidence for elastic bill settlement. Unfortunately, existing data auditing schemes do not sufficiently consider timeliness during auditing. Furthermore, practical data auditing schemes should have the capability to check the integrity of scalable data. In this paper, we propose a blockchain-based dynamic data auditing scheme with strong timeliness to ensure that data stored in outsourcing storage systems remain intact. Our scheme encapsulates timestamps into homomorphic verifiable tags to simultaneously check data integrity and timestamp validity. To achieve dynamicity, we utilize the Merkle hash tree to store the tags, allowing for block-level dynamic operations. Additionally, by leveraging the transparency, non-repudiation, and tamper resistance of blockchain technology, we design a blockchain-based data auditing framework to prevent malicious behavior from all entities. We then formally prove the soundness and privacy of our scheme. Finally, we conduct theoretical analysis and experimental evaluation to demonstrate that the performance of our scheme is of acceptable efficiency to existing works in terms of computation cost, communication overhead, and storage overhead.
Keyword ：
blockchain blockchain dynamic data auditing dynamic data auditing Outsourcing storage Outsourcing storage timeliness timeliness
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Chuan , Xuan, Haojun , Wu, Tong et al. Blockchain-Based Dynamic Time-Encapsulated Data Auditing for Outsourcing Storage [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 1979-1993 . |
| MLA | Zhang, Chuan et al. "Blockchain-Based Dynamic Time-Encapsulated Data Auditing for Outsourcing Storage" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 1979-1993 . |
| APA | Zhang, Chuan , Xuan, Haojun , Wu, Tong , Liu, Ximeng , Yang, Guomin , Zhu, Liehuang . Blockchain-Based Dynamic Time-Encapsulated Data Auditing for Outsourcing Storage . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 1979-1993 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first presents a federated GAN algorithm using the divide-and-conquer strategy that eliminates the problem of model collapse in non-IID settings. To guarantee user-level privacy, FlGan then exploits Fully Homomorphic Encryption (FHE) to design the privacy-preserving GAN augmentation method for the unbiased FL. Extensive experiments show that FlGan achieves unbiased FL with $10\%-60\%$10%-60% accuracy improvement compared with two state-of-the-art FL baselines (i.e., FedAvg and FedSGD) trained under different non-IID settings. The FHE-based privacy guarantees only cost about 0.53% of the total overhead in FlGan.
Keyword ：
Computational modeling Computational modeling Convergence Convergence Data models Data models Federated learning Federated learning fully homomorphic encryption fully homomorphic encryption GAN GAN Generative adversarial networks Generative adversarial networks non-IID non-IID Privacy Privacy Servers Servers Training Training user-level privacy user-level privacy
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ma, Zhuoran , Liu, Yang , Miao, Yinbin et al. FlGan: GAN-Based Unbiased Federated Learning Under Non-IID Settings [J]. | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2024 , 36 (4) : 1566-1581 . |
| MLA | Ma, Zhuoran et al. "FlGan: GAN-Based Unbiased Federated Learning Under Non-IID Settings" . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 36 . 4 (2024) : 1566-1581 . |
| APA | Ma, Zhuoran , Liu, Yang , Miao, Yinbin , Xu, Guowen , Liu, Ximeng , Ma, Jianfeng et al. FlGan: GAN-Based Unbiased Federated Learning Under Non-IID Settings . | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2024 , 36 (4) , 1566-1581 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Federated Learning (FL) eliminates data silos that hinder digital transformation while training a shared global model collaboratively. However, training a global model in the context of FL has been highly susceptible to heterogeneity and privacy concerns due to discrepancies in data distribution, which may lead to potential data leakage from uploading model updates. Despite intensive research on above-identical issues, existing approaches fail to balance robustness and privacy in FL. Furthermore, limiting model updates or iterative clustering tends to fall into local optimum problems in heterogeneous (Non-IID) scenarios. In this work, to address these deficiencies, we provide lightweight privacy-preserving cross-cluster federated learning (PrivCrFL) on Non-IID data, to trade off robustness and privacy in Non-IID settings. Our PrivCrFL exploits secure one-shot hierarchical clustering with cross-cluster shifting for optimizing sub-group convergences. Furthermore, we introduce intra-cluster learning and inter-cluster learning with separate aggregation for mutual learning between each group. We perform extensive experimental evaluations on three benchmark datasets and compare our results with state-of-the-art studies. The findings indicate that PrivCrFL offers a notable performance enhancement, with improvements ranging from 0.26%~uparrow to 1.35%~uparrow across different Non-IID settings. PrivCrFL also demonstrates a superior communication compression ratio in secure aggregation, outperforming current state-of-the-art works by 10.59%. © 2005-2012 IEEE.
Keyword ：
Distributed machine learning Distributed machine learning federated learning federated learning heterogeneity data heterogeneity data privacy-preserving privacy-preserving
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Z. , Yu, S. , Chen, F. et al. Lightweight Privacy-Preserving Cross-Cluster Federated Learning With Heterogeneous Data [J]. | IEEE Transactions on Information Forensics and Security , 2024 , 19 : 7404-7419 . |
| MLA | Chen, Z. et al. "Lightweight Privacy-Preserving Cross-Cluster Federated Learning With Heterogeneous Data" . | IEEE Transactions on Information Forensics and Security 19 (2024) : 7404-7419 . |
| APA | Chen, Z. , Yu, S. , Chen, F. , Wang, F. , Liu, X. , Deng, R.H. . Lightweight Privacy-Preserving Cross-Cluster Federated Learning With Heterogeneous Data . | IEEE Transactions on Information Forensics and Security , 2024 , 19 , 7404-7419 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
As the scale of the networks continually expands, the detection of distributed denial of service (DDoS) attacks has become increasingly vital. We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network (DNN). The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible, thereby reducing data volume. Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic, enhancing the neural network’s generalization capabilities. Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic. Compared with the benchmark methods, our method reaches 99.9% on low-rate DDoS (LDDoS), flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%, 31% and 8%. © 2024 Tech Science Press. All rights reserved.
Keyword ：
DDoS DDoS DNN DNN improved generalized entropy improved generalized entropy real-time real-time
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Y. , Han, Y. , Chen, H. et al. IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN [J]. | Computers, Materials and Continua , 2024 , 80 (2) : 1851-1866 . |
| MLA | Liu, Y. et al. "IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN" . | Computers, Materials and Continua 80 . 2 (2024) : 1851-1866 . |
| APA | Liu, Y. , Han, Y. , Chen, H. , Zhao, B. , Wang, X. , Liu, X. . IGED: Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN . | Computers, Materials and Continua , 2024 , 80 (2) , 1851-1866 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Boolean range query (BRQ) is a typical type of spatial keyword query that is widely used in geographic information systems, location-based services and other applications. It retrieves the objects inside the query range and containing all query keywords. Many privacy-preserving BRQ schemes have been proposed to support BRQ over encrypted data. However, most of them fail to achieve efficient retrieval and lightweight result verification while suppressing access and search pattern leakage. Thus, in this paper, we propose an efficient verifiable privacy-preserving Boolean range query with suppressed leakage. Firstly, we convert BRQ into multi-keyword query by using Gray code and Bloom filter. Then, we achieve efficient oblivious multi-keyword query by combining distributed point function and PRP-based Cuckoo hashing, which protects the access and search patterns. Moreover, we support lightweight and oblivious result verification based on oblivious query, aggregate MAC, keyed-hashing MAC and XOR-homomorphic pseudorandom function. It enables query users to verify the result integrity with a proof whose size is independent of the size of the outsourced dataset. Finally, formal security analysis and extensive experiments demonstrate that our proposed scheme is adaptively secure and efficient for practical applications, respectively.
Keyword ：
access pattern access pattern Aggregates Aggregates Cryptography Cryptography Hash functions Hash functions Indexes Indexes Privacy Privacy Privacy-preserving Boolean range query Privacy-preserving Boolean range query Query processing Query processing result verification result verification search pattern search pattern Search problems Search problems
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Tong, Qiuyun , Li, Xinghua , Miao, Yinbin et al. Beyond Result Verification: Efficient Privacy-Preserving Spatial Keyword Query With Suppressed Leakage [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 2746-2760 . |
| MLA | Tong, Qiuyun et al. "Beyond Result Verification: Efficient Privacy-Preserving Spatial Keyword Query With Suppressed Leakage" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 2746-2760 . |
| APA | Tong, Qiuyun , Li, Xinghua , Miao, Yinbin , Wang, Yunwei , Liu, Ximeng , Deng, Robert H. . Beyond Result Verification: Efficient Privacy-Preserving Spatial Keyword Query With Suppressed Leakage . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 2746-2760 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Federated learning (FL) allows multiple clients to train deep learning models collaboratively while protecting sensitive local datasets. However, FL has been highly susceptible to security for federated backdoor attacks (FBA) through injecting triggers and privacy for potential data leakage from uploaded models in practical application scenarios. FBA defense strategies consider specific and limited attacker models, and a sufficient amount of noise injected can only mitigate rather than eliminate the attack. To address these deficiencies, we introduce a Robust Federated Backdoor Defense Scheme (RFBDS) and Privacy preserving RFBDS (PrivRFBDS) to ensure the elimination of adversarial backdoors. Our RFBDS to overcome FBA consists of amplified magnitude sparsification, adaptive OPTICS clustering, and adaptive clipping. The experimental evaluation of RFBDS is conducted on three benchmark datasets and an extensive comparison is made with state-of-the-art studies. The results demonstrate the promising defense performance from RFBDS, moderately improved by 31.75% similar to 73.75% in clustering defense methods, and 0.03% similar to 56.90% for Non-IID to the utmost extent for the average FBA success rate over MNIST, FMNIST, and CIFAR10. Besides, our privacy-preserving shuffling in PrivRFBDS maintains is 7.83e-5 similar to 0.42x that of state-of-the-art works.
Keyword ：
backdoor defense backdoor defense distributed backdoor attack distributed backdoor attack Federate learning Federate learning heterogeneity data heterogeneity data privacy-preserving privacy-preserving
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Chen, Zekai , Yu, Shengxing , Fan, Mingyuan et al. Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 693-707 . |
| MLA | Chen, Zekai et al. "Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 693-707 . |
| APA | Chen, Zekai , Yu, Shengxing , Fan, Mingyuan , Liu, Ximeng , Deng, Robert H. . Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 693-707 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Users in dynamic spectrum access (DSA) with federated reinforcement learning (FRL) autonomously access channels, avoiding centralized coordination and protecting users' privacy. However, existing FRL-based DSA mechanisms are limited to ideal network states, i.e., assuming that channel states and users' interference relationships are unchanged. Besides, users should upload intermediate results simultaneously for federated aggregation. The above conditions are impractical for mobile users since their network states and locations are unstable. Meanwhile, newly connected users have to train their models through local data with numerous computing resources since global models are unsuitable for them. We propose FRDSA, an FRL-based secure and lightweight channel selection mechanism in DSA for mobile users under dynamic network states. An independent channel selection environment with a virtual group strategy is presented to avoid interference between users under unstable channel states. Furthermore, an asynchronous parameter aggregation method in FRDSA dynamically adjusts the aggregation factors without users simultaneously uploading intermediate results. Simulations based on real trajectory data show that FRDSA significantly reduces approximately 60% interference between mobile users under unstable network states. Newly connected users can directly apply the well-trained global model to access channels autonomously instead of retraining a model, effectively reducing mobile users' computing resource requirements.
Keyword ：
Dynamic spectrum access Dynamic spectrum access federated reinforcement learning federated reinforcement learning location privacy location privacy mobile users mobile users
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Dong, Xuewen , You, Zhichao , Liu, Ximeng et al. Federated and Online Dynamic Spectrum Access for Mobile Secondary Users [J]. | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2024 , 23 (1) : 621-636 . |
| MLA | Dong, Xuewen et al. "Federated and Online Dynamic Spectrum Access for Mobile Secondary Users" . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 23 . 1 (2024) : 621-636 . |
| APA | Dong, Xuewen , You, Zhichao , Liu, Ximeng , Guo, Yuanxiong , Shen, Yulong , Gong, Yanmin . Federated and Online Dynamic Spectrum Access for Mobile Secondary Users . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2024 , 23 (1) , 621-636 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
