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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

Journal of computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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DM-FedMF: A Recommendation Model of Federated Matrix Factorization with Detection Mechanism

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IEEE Transactions on network Science and Engineering 2025年

作者： Zheng, Xiaoyao Jia, Xianmin Cheng, Xiongchao He, Wenxuan Sun, Liping Guo, Liangmin Yu, Qingying Luo, Yonglong Anhui Normal University Anhui Provincial Key Laboratory of Network and Information Security School of Computer and Information Wuhu China

Items are recommended to users by the federated recommendation system while protecting user privacy, but there is a risk of the performance of the global model being seriously affected by malicious clients through the tampering of local data and model parameters. In this paper, a federated matrix factorization recommendation model with a detection mechanism(DM-FedMF) is proposed. The experimental analysis concludes that there is a gradient difference in item preference parameters between malicious and benign clients. Accordingly, an objective function is designed to measure item preference differences as a means of identifying malicious clients on the server. Secondly, a malicious client reporting mechanism is proposed to count the reported frequency of all clients and set a threshold. Based on the number of honest clients, the list of attackers is updated. Finally, the malicious client is detected and eliminated based on the list of attackers. The other three defense algorithms are compared with two public datasets in this paper. The experimental results show that the detection mechanism can effectively defend against data poisoning attacks, category attacks, noise attacks, and sign flipping attacks, and the performance of the model's recommendations is better than that achieved by applying other defense methods. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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A New High-Capacity Robust Image Steganography Method via Adversarial network 5

A New High-Capacity Robust Image Steganography Method via Ad...

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5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023

作者： Dong, Weina Jiang, Zongbao Chen, Lifeng Sun, Wenquan University of PAP Key Laboratory of Network and Information Security of PAP Engineering Shaanxi Xi'an710086 China

ISBN: (纸本)9798350357738

Aiming at the problem that the high-capacity steganography model based on encoding-decoding network had weak robustness and could not resist noise attack and channel compression, an improved high-capacity robust image steganography algorithm based on adversarial network was proposed. In the proposed algorithm, encoder, decoder and critic based on densely connected convolutional networks (DenseNet) were designed. The secret information and the carrier image are jointly encoded into a steganographic image by the encoder, then the secret information was extracted by the decoder. The critic was used to discriminate between carrier images and steganographic images. A noise layer was added between encoder and decoder, Dropout, JPEG compression, gaussian blur, gaussian noise and salt and pepper noise were used to simulate a real environment of various kinds of noise attack. The steganography output by encoder was processed with different kinds of noise and decoded by decoder. Through model was trained, the message could be extracted from the noise-processed steganographic image by the decoder, so the ability to resist noise attacks was realized. The experiment results show that the steganographic capacity of the proposed algorithm reaches 0.450.95 bits per pixel (bpp) on 360 × 360 pixel images, which are 3.6 times higher than that of the earlier robust steganographic algorithms. The decoding accuracy reaches 0.72-0.97, which are 42% points higher than that of steganography without noise layer. The proposed algorithm not only guarantees high embedding quantity and high coding image quality, but also has stronger anti-noise capability. © 2023 IEEE.

关键词： Steganography

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Reversible Data Hiding in Encrypted Images based on Classic McEliece Cryptosystem 2

Reversible Data Hiding in Encrypted Images based on Classic ...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Jiang, Zongbao Zhang, Minqing Di, Fuqiang Dong, Weina University of PAP Key Laboratory of Network and Information Security of PAP Engineering Shaanxi Xi'an710086 China

ISBN: (纸本)9798350331417

Aiming at the problems that the security of traditional data hiding algorithms is difficult to be guaranteed and the existing algorithms' embedded volume is restricted under quantum operation attacks, a reversible data hiding algorithm in encrypted images based on Classic McEliece cryptosystem is proposed. The image pixels are first decomposed into bit planes to generate a data stream, and then encrypted in groups using the Classic McEliece cipher algorithm. In the encryption process, the fault tolerance mechanism in its encryption algorithm is utilized to select the appropriate modulation noise composition and the mapping relationship between the secret data, and the error pattern is modulated into noise to interfere with the encryption process, so as to realize the embedding of the secret data. Finally, the marked encrypted image is obtained by using the exclusive-or encryption and scrambling encryption processes. The experimental results show that the algorithm has the reversibility of carrier recovery. Compared with the existing algorithms, this algorithm's embedded volume is advantageous, and its highest embedding rate is 4.43548 bpp, which realizes large-capacity data hiding while improving security. © 2023 IEEE.

关键词： Classic McEliece error-correcting codes high-capacity hiding reversible data hiding in encrypted images

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Joint Entity and Relation Extraction Based on Prompt Learning and Multi-channel Heterogeneous Graph Enhancement 22

Joint Entity and Relation Extraction Based on Prompt Learnin...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Lv, Haiqing Han, Xiaohui Wang, Peipei Zuo, Wenbo Guo, Jing Xu, Lijuan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China Quan Cheng Laboratory Jinan China Jinan China

ISBN: (纸本)9798331509712

Joint extraction of entity and relation is crucial in information extraction, aiming to extract all relation triples from unstructured text. However, current joint extraction methods face two main issues. Firstly, they rarely consider the semantic information of entity and relation labels, leading to models that fail to fully understand and utilize the rich semantics in these labels, thereby limiting their performance. Secondly, although table-filling methods are widely used, they focus only on the start or end positions and ignore deep interactions between tables, relying solely on word-level information. To address these issues, we propose the P-MHE framework based on prompt learning and multi-channel heterogeneous graph enhancement. First, we use prompt templates to construct semantic nodes for entity and relation type labels, initializing them along with words as nodes in a heterogeneous graph. We iteratively fuse these semantic nodes through a message-passing mechanism to obtain node representations suitable for entity and relation extraction tasks. Secondly, we design a multi-channel heterogeneous graph to model node relationships from different perspectives, enhancing feature interactions among different types of nodes. Finally, we aggregate the semantic node information of entity and relation type labels after iteration, constructing separate decoding tables for each entity and relation type to better adapt to their respective characteristics. We evaluated our model on four public datasets. Experimental results show that P-MHE outperforms existing models on multiple public datasets. Extensive additional experiments further validate the effectiveness of our model. © 2024 IEEE.

关键词： Knowledge graph

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New results on quantum boomerang attacks

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Quantum information Processing 2023年第4期22卷 1-27页

作者： Zou, Hongkai Zou, Jian Luo, Yiyuan College of Computer and Data Science Fuzhou University Fuzhou China Key Lab of Information Security of Network Systems Fuzhou University Fuzhou China School of Information Sciences and Technology Huizhou University Huizhou China

At SAC 2021, Frixons et al. proposed quantum boomerang attacks that can effectively recover the keys of block ciphers in the quantum setting. Based on their work, we further consider how to quantize the generic boomerang attacks proposed by Biham et al. at FSE 2002, so as to obtain more generic quantum boomerang attacks. Similar to Frixons et al.’s work, we only consider quantum key recovery attacks in the single-key setting. With the help of some famous quantum algorithms, this paper presents two methods to convert the attacks of Biham et al. into some new quantum key recovery attacks. In order to proof our methods, we apply our new ideas to attack Serpent-256 and ARIA-196. To sum up, for Serpent-256, we give valid 9-round and 10-round quantum key recovery attacks respectively. The quantum time complexity of 9-round and 10-round of Serpent-256 is 2 115.43 and 2 126.6 respectively. Furthermore, we show a valid quantum key attack on 6-round ARIA-196 which has a time complexity of 2 89.8 with negligible memory. The time complexity of the above quantum attacks are better than the corresponding classical attacks and quantum generic key recovery attack via Grover’s algorithm. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Quantum theory

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A Label Flipping Attack on Machine Learning Model and Its Defense Mechanism 22nd

A Label Flipping Attack on Machine Learning Model and Its De...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Li, Qingru Wang, Xinru Wang, Fangwei Wang, Changguang Key Laboratory of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyberspace Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9783031226762

Recently, the robustness of machine learning against data poisoning attacks is widely concerned. As a subclass of poisoning attack, the label flipping attack can poison training data resulting in reducing the classification performance of training model. This attack poses a more serious threat in complex network or high-noise environments, such as in the environment of Internet of Things. In this paper, a new label flipping attack method and its defense strategy are proposed. First, a label flipping attack based on agglomerative hierarchical clustering is proposed. The attack uses agglomerative hierarchical clustering to identify vulnerable samples in training data and then carries out label flipping on them. To defend against this attack, a TrAdaBoost-based label correction defense method is proposed. This method uses the TrAdaBoost algorithm to update the weight of the contaminated data, and then uses the updated weight value to judge and remark the contaminated training samples. The contaminated samples are cleaned and used to retrain the classifier. Compared with the state-of-the-art methods, the proposed attack strategy can reduce the accuracy of the model more effectively and the proposed defense method can better protect the classification model. © 2023, Springer Nature Switzerland AG.

关键词： Machine learning

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An Efficient and Secure Data Audit Scheme for Cloud-Based EHRs with Recoverable and Batch Auditing

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computers, Materials & Continua 2025年第4期83卷 1533-1553页

作者： Yuanhang Zhang Xu An Wang Weiwei Jiang Mingyu Zhou Xiaoxuan Xu Hao Liu College of Cryptography Engineering Engineering University of People’s Armed PoliceXi’an710086China Key Laboratory of Network and Information Security Engineering University of People’s Armed PoliceXi’an710086China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing100876China

Cloud storage,a core component of cloud computing,plays a vital role in the storage and management of *** Health Records(EHRs),which document users’health information,are typically stored on cloud ***,users’sensitive data would then become *** the event of data loss,cloud storage providers might conceal the fact that data has been compromised to protect their reputation and mitigate *** the integrity of data stored in the cloud remains a pressing issue that urgently needs to be *** this paper,we propose a data auditing scheme for cloud-based EHRs that incorporates recoverability and batch auditing,alongside a thorough security and performance *** scheme builds upon the indistinguishability-based privacy-preserving auditing approach proposed by Zhou et *** identify that this scheme is insecure and vulnerable to forgery attacks on data storage *** address these vulnerabilities,we enhanced the auditing process using masking techniques and designed new algorithms to strengthen *** also provide formal proof of the security of the signature algorithm and the auditing ***,our results show that our scheme effectively protects user privacy and is resilient against malicious *** results indicate that our scheme is not only secure and efficient but also supports batch auditing of cloud ***,when auditing 10,000 users,batch auditing reduces computational overhead by 101 s compared to normal auditing.

关键词： security cloud computing cloud storage recoverable batch auditing

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Privacy-Preserving Searchable Encryption Scheme Based on Public and Private Blockchains

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Tsinghua Science and Technology 2023年第1期28卷 13-26页

作者： Ruizhong Du Caixia Ma Mingyue Li School of Cyberspace Security and Computer and Hebei Province Key Laboratory of High Confidence Information SystemHebei UniversityBaoding 071002China College of Cyber Science Nankai UniversityTianjin 300350China

While users enjoy the convenience of data outsourcing in the cloud,they also face the risks of data modification and private information *** encryption technology can perform keyword searches over encrypted data while protecting their privacy and guaranteeing the integrity of the data by verifying the search ***,some associated problems are still encountered,such as the low efficiency of verification and uncontrollable query ***,this paper proposes a Privacy-Preserving Searchable Encryption(PPSE)scheme based on public and private ***,we store an encrypted index in a private blockchain while outsourcing corresponding encrypted documents to a public *** encrypted documents are located through the encrypted *** method can reduce the storage overhead on the blockchains,and improve the efficiency of transaction execution and the security of stored ***,we adopt a smart contract to introduce a secondary verification access control mechanism and restrict data users’access to the private blockchain through authorization for the purpose of guaranteeing data privacy and the correctness of access control ***,the security analysis and experimental results indicate that compared with existing schemes,the proposed scheme can not only improve the security of encrypted data but also guarantee the efficiency of the query.

关键词： private blockchain public blockchain access control forward privacy backward privacy

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Textual Data De-Privatization Scheme Based on Generative Adversarial networks 24th

Textual Data De-Privatization Scheme Based on Generative Adv...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Du, Yanning Xu, Jinnan Zhang, Yaling Wang, Yichuan Wang, Zhoukai School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

ISBN: (纸本)9789819615506

In many fields, such as healthcare, finance, and scientific research, data sharing and collaboration are critical to achieving better outcomes. However, the sharing of personal data often involves privacy risks, so privacy-preserving techniques are needed to ensure data security and privacy. The superior performance and flexibility of generative models in data representation have led to significant progress in the development of data privacy. This paper proposes a textual data de-privatization scheme based on generative adversarial networks that well combines generative adversarial networks with privacy protection. The generated de-privacy data retains the statistical properties of the original data and effectively removes sensitive information. Moreover, feature sorting and reshaping modules are introduced to enable the generator to better capture the relationships between features, thus improving the quality of synthetic data. In this paper, the utility of synthetic data is evaluated from three aspects, including similarity assessment, privacy assessment, and model utility assessment. Experimental results show that this method achieves a good trade-off in terms of textual data privacy protection and data quality maintenance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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