IEEE Transactions on Information Forensics and Security最新文献_第7页

Privacy for Free: Spy Attack in Vertical Federated Learning by Both Active and Passive Parties

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-27 DOI: 10.1109/tifs.2025.3534469

Chaohao Fu, Hongbin Chen, Na Ruan

引用次数: 0

SMSSE: Size-pattern Mitigation Searchable Symmetric Encryption

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-27 DOI: 10.1109/tifs.2025.3534467

Yang Yang, Haihui Fan, Jinchao Zhang, Bo Li, Hui Ma, Xiaoyan Gu

引用次数: 0

Anonymous and Efficient (t, n)-Threshold Ownership Transfer for Cloud EMRs Auditing

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-27 DOI: 10.1109/TIFS.2025.3534563

Yamei Wang;Yuexin Zhang;Ayong Ye;Jian Shen;Derui Wang;Yang Xiang

{"title":"Anonymous and Efficient (t, n)-Threshold Ownership Transfer for Cloud EMRs Auditing","authors":"Yamei Wang;Yuexin Zhang;Ayong Ye;Jian Shen;Derui Wang;Yang Xiang","doi":"10.1109/TIFS.2025.3534563","DOIUrl":"10.1109/TIFS.2025.3534563","url":null,"abstract":"In cloud Electronic Medical Records (EMRs), health-related private information such as genetics and diseases is contained. Thus, the secure ownership transfer protocol should protect users’ privacy. In certain scenarios, some users, including patients, doctors, medical and research institutions, may be offline. As a result, existing protocols cannot be directly employed. Motivated by these observations, in this paper we propose a secure and efficient ownership transfer for cloud EMRs auditing protocol. Specifically, our protocol allows the existence of offline users while ensuring users anonymity, it is achieved using different signature constructions. Additionally, a tracing mechanism is introduced to safeguard against malicious users. We rigorously prove the security of our protocol, comprehensively evaluate the performance of it, and compare our protocol with a few closely relevant protocols. According to the evaluations, our protocol significantly improves ownership transfer efficiency while achieving additional functionalities, including public verifiability, multi-ownership transferability, anonymity, and traceability.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1710-1723"},"PeriodicalIF":6.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scope: On Detecting Constrained Backdoor Attacks in Federated Learning

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-24 DOI: 10.1109/tifs.2025.3533899

Siquan Huang, Yijiang Li, Xingfu Yan, Ying Gao, Chong Chen, Leyu Shi, Biao Chen, Wing W.Y. Ng

引用次数: 0

Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-24 DOI: 10.1109/tifs.2025.3533907

Yu Jiang, Jiyuan Shen, Ziyao Liu, Chee Wei Tan, Kwok-Yan Lam

引用次数: 0

Parameter Interpolation Adversarial Training for Robust Image Classification

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-24 DOI: 10.1109/TIFS.2025.3533925

Xin Liu;Yichen Yang;Kun He;John E. Hopcroft

{"title":"Parameter Interpolation Adversarial Training for Robust Image Classification","authors":"Xin Liu;Yichen Yang;Kun He;John E. Hopcroft","doi":"10.1109/TIFS.2025.3533925","DOIUrl":"10.1109/TIFS.2025.3533925","url":null,"abstract":"Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has been demonstrated to be the most effective method to defend against adversarial attacks. However, existing adversarial training methods show that the model robustness has apparent oscillations and overfitting issues in the training process, degrading the defense efficacy. To address these issues, we propose a novel framework called Parameter Interpolation Adversarial Training (PIAT). PIAT tunes the model parameters between each epoch by interpolating the parameters of the previous and current epochs. It makes the decision boundary of model change more moderate and alleviates the overfitting issue, helping the model converge better and achieving higher model robustness. In addition, we suggest using the Normalized Mean Square Error (NMSE) to further improve the robustness by aligning the relative magnitude of logits between clean and adversarial examples rather than the absolute magnitude. Extensive experiments conducted on several benchmark datasets demonstrate that our framework could prominently improve the robustness of both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1613-1623"},"PeriodicalIF":6.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Physical Layer Authentication With Dynamic Soft Voting for Smart Distribution Grids

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-24 DOI: 10.1109/TIFS.2025.3533914

You Li;Yan Huo;Tianhui Zhang;Zhongguo Zhou;Qinghe Gao;Tao Yan;Yongning Yang;Tao Jing

{"title":"Distributed Physical Layer Authentication With Dynamic Soft Voting for Smart Distribution Grids","authors":"You Li;Yan Huo;Tianhui Zhang;Zhongguo Zhou;Qinghe Gao;Tao Yan;Yongning Yang;Tao Jing","doi":"10.1109/TIFS.2025.3533914","DOIUrl":"10.1109/TIFS.2025.3533914","url":null,"abstract":"The smart distribution grid (SDG), characterized by large-scale interconnections and strong dependence on information and communication technologies, is highly susceptible to potential security threats, such as spoofing attacks and man-in-the-middle attacks. These threats may lead to the leakage of sensitive user power-expenditure information, even cause great economic damage. Therefore, authentication is of utmost importance in guaranteeing the electrical safety of SDGs. In this paper, we present a distributed physical layer authentication (DPLA) scheme tailored for smart meter authentication. The scheme overcomes the limitations of traditional upper-layer cryptography-based mechanisms, and achieves lightweight continuous authentication in a cooperative manner. To fully exploit the channel information collected by collaborative nodes located in different azimuths, a CNN algorithm is designed for deep feature extraction. Moreover, a situational-aware dynamic weighted voting strategy is introduced to coordinate inconsistent opinions, thereby making unified decisions. Aimed at maximizing the integrated performance gains of DPLA, both long-term reputation and short-term performance are taken into account for node’s weight update. Finally, simulations are carried out. The results demonstrate that our scheme outperforms DPLAs based on static voting strategies with respect to authentication accuracy, anti-disturbance robustness and environmental adaptability; Hence, it caters to the demand for high-quality continuous authentication in SDGs.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1807-1821"},"PeriodicalIF":6.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Supervised Locality-Sensitive Deep Hashing for the Robust Retrieval of Degraded Images

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-23 DOI: 10.1109/TIFS.2025.3531104

Lingyun Xiang;Hailang Hu;Qian Li;Hao Yu;Xiaobo Shen

{"title":"Self-Supervised Locality-Sensitive Deep Hashing for the Robust Retrieval of Degraded Images","authors":"Lingyun Xiang;Hailang Hu;Qian Li;Hao Yu;Xiaobo Shen","doi":"10.1109/TIFS.2025.3531104","DOIUrl":"10.1109/TIFS.2025.3531104","url":null,"abstract":"Recently, numerous degraded images have flooded search engines and social networks, finding extensive and practical applications in the real world. However, these images have also posed new challenges to conventional image retrieval tasks. To this end, we introduce a new task of retrieving degraded images through deep hashing from large-scale databases, and further present the Locality-Sensitive Hashing Network (LSHNet) to tackle it in a self-supervised manner. More specifically, we first propose a triplet strategy to enable the self-supervised training of LSHNet in an end-to-end fashion. Due to the designed strategy, the highly semantic similarity and discrimination of degraded images are well-preserved in our learned latent codes without requiring additional human labor in labeling tons of degraded images. Moreover, to tackle large-scale image retrieval efficiently, we further propose to transform the latent codes into locality-sensitive hashing codes such that the degraded images can be retrieved in sublinear time with their representation ability almost unaffected. Extensive experiments are conducted on three public benchmarks where the results demonstrate the superior performance of LSHNet in retrieving similar images under degraded conditions.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1582-1596"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Collaborative and Cross-Environment UAV Classification: Federated Semantic Regularization

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-23 DOI: 10.1109/TIFS.2025.3531773

Xue Fu;Yu Wang;Yun Lin;Tomoaki Ohtsuki;Bamidele Adebisi;Guan Gui;Hikmet Sari

{"title":"Toward Collaborative and Cross-Environment UAV Classification: Federated Semantic Regularization","authors":"Xue Fu;Yu Wang;Yun Lin;Tomoaki Ohtsuki;Bamidele Adebisi;Guan Gui;Hikmet Sari","doi":"10.1109/TIFS.2025.3531773","DOIUrl":"10.1109/TIFS.2025.3531773","url":null,"abstract":"The rapid and widespread adoption of unmanned aerial vehicles (UAVs) poses significant threats to public safety and security in sensitive areas and subsequently underscores the urgent need for effective UAV surveillance solutions, where UAV classification emerges as a vital technology. Deep learning (DL) methods can autonomously extract implicit features from UAV signals and subsequently infer their types, provided that sufficient signal samples are available. Due to the high mobility of UAVs, it is challenging to ensure continuous monitoring between UAVs and the surveillance system to obtain sufficient samples. Moreover, DL models developed from sufficient but environment-specific datasets tend to be less generalized. This paper proposes a novel federated semantic regularization for learning an UAV classification model and further classifying UAVs across diverse environmental conditions. The approach enhances model generalization by regularizing semantic features during the local model training process on each participant. Subsequently, these local models are aggregated into a robust global model. Extensive testing across multiple environments demonstrates the superior classification performance of our approach compared to existing non-federated and federated approaches. The average classification accuracy of the proposed method in the three environments is 95.68%, which is improved by 13.39% compared to the non-federated methods and by 2.75% compared to the federated methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1624-1635"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying Privacy Risks of Behavioral Semantics in Mobile Communication Services

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-23 DOI: 10.1109/TIFS.2025.3533144

Guoying Qiu;Tiecheng Bai;Guoming Tang;Deke Guo;Chuandong Li;Yan Gan;Baoping Zhou;Yulong Shen

{"title":"Quantifying Privacy Risks of Behavioral Semantics in Mobile Communication Services","authors":"Guoying Qiu;Tiecheng Bai;Guoming Tang;Deke Guo;Chuandong Li;Yan Gan;Baoping Zhou;Yulong Shen","doi":"10.1109/TIFS.2025.3533144","DOIUrl":"10.1109/TIFS.2025.3533144","url":null,"abstract":"Location-based mobile services, while improving user daily life, also raise significant privacy concerns in the sharing of location data. These trajectories indicate users’ traveling behavioural traces with rich semantics derived from open-source information. Behavioral-semantic analysis reveals users’ travelling motivations and underlying behavioral patterns. It contributes to attackers launching inferential attacks for behavior prediction, identity identification, or other privacy invasions, even when the location data is protected. It remains open to the issues of behavioral-semantic privacy-risk quantification and privacy-protection evaluation. This paper aims to reveal such semantic privacy risks of user behaviors arising from the publication of location trajectories in mobile scenarios. We formalize user semantic-mobility process to analyze his underlying behavior patterns. Then, we design semantic inference algorithms conditional on the released trajectory to reason about the observation-based likelihood of the user’s actual staying and transfer behaviours and behavioural-trace tracking. Extensive experiments with real-world data demonstrate their performance on inference accuracy and semantic similarity, offering a quantification criterion for deploying mobile privacy protection.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"1908-1923"},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143027184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0