Pub Date : 2026-03-05DOI: 10.1109/TIFS.2026.3671133
Tianming Hou;Hui Ma;Jinchao Zhang;Yang Li;Bo Li;Weiping Wang
Redactable blockchains preserve the integrity of hash links while enabling authorized redactions to comply with regulatory requirements. However, existing permissioned solutions suffer from three severe issues. First, fine-grained privilege control incurs significant storage overhead, especially in the case of single-use authorization. Second, the reliance on bilinear pairings in chameleon hash leads to significant performance degradation when handling large-scale redaction requests. Finally, multiple incorporated components often unconsciously introduce centralized entities, undermining the decentralized nature. In this paper, we propose MithrilRB, a resource-efficient and decentralized redactable blockchain with single-use authorization. Specifically, we introduce a privilege control mechanism with our proposed multi-authority attribute-based signature (MA-ABS) and the threshold BLS signature, achieving fine-grained single-use authorization and direct user revocation without extra ciphertext storage. We also design a pairing-free non-interactive threshold chameleon hash (PNITCH), which enhances efficiency and is better suited for large-scale redaction requests. Moreover, MithrilRB eliminates centralized trust points that hold secret information, ensuring fully decentralization-compatible functional integration in redactable blockchains. Finally, we implement MithrilRB, and the experimental results demonstrate that MithrilRB significantly outperforms existing solutions in both computational efficiency and storage requirements.
{"title":"MithrilRB: Resource-Efficient Redactable Blockchain With Single-Use Authorization","authors":"Tianming Hou;Hui Ma;Jinchao Zhang;Yang Li;Bo Li;Weiping Wang","doi":"10.1109/TIFS.2026.3671133","DOIUrl":"10.1109/TIFS.2026.3671133","url":null,"abstract":"Redactable blockchains preserve the integrity of hash links while enabling authorized redactions to comply with regulatory requirements. However, existing permissioned solutions suffer from three severe issues. First, fine-grained privilege control incurs significant storage overhead, especially in the case of single-use authorization. Second, the reliance on bilinear pairings in chameleon hash leads to significant performance degradation when handling large-scale redaction requests. Finally, multiple incorporated components often unconsciously introduce centralized entities, undermining the decentralized nature. In this paper, we propose MithrilRB, a resource-efficient and decentralized redactable blockchain with single-use authorization. Specifically, we introduce a privilege control mechanism with our proposed multi-authority attribute-based signature (MA-ABS) and the threshold BLS signature, achieving fine-grained single-use authorization and direct user revocation without extra ciphertext storage. We also design a pairing-free non-interactive threshold chameleon hash (PNITCH), which enhances efficiency and is better suited for large-scale redaction requests. Moreover, MithrilRB eliminates centralized trust points that hold secret information, ensuring fully decentralization-compatible functional integration in redactable blockchains. Finally, we implement MithrilRB, and the experimental results demonstrate that MithrilRB significantly outperforms existing solutions in both computational efficiency and storage requirements.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2698-2712"},"PeriodicalIF":8.0,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371510","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}
{"title":"X 2 O: Cross Parallel Optimization of the CROSS Post-Quantum Scheme on GPU","authors":"Yijing Ning, Jiankuo Dong, Yajie Zhao, Jingqiang Lin, Tian Zhou, Jiachen Wang, Fu Xiao","doi":"10.1109/tifs.2026.3671118","DOIUrl":"https://doi.org/10.1109/tifs.2026.3671118","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"56 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371512","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}
Pub Date : 2026-03-05DOI: 10.1109/tifs.2026.3671089
Yongkang Ding, Zi Ye, Ivonne Xu, Shuangquan Lyu, Liyan Zhang
{"title":"Multi-Scale Adaptive Clustering and Local Consistency Learning for Unsupervised Clothing-Changing Person Re-Identification","authors":"Yongkang Ding, Zi Ye, Ivonne Xu, Shuangquan Lyu, Liyan Zhang","doi":"10.1109/tifs.2026.3671089","DOIUrl":"https://doi.org/10.1109/tifs.2026.3671089","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"6 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371506","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}
Pub Date : 2026-03-05DOI: 10.1109/TIFS.2026.3671097
Kaixuan Huang;Yongtao Ma;Jialu Zhu;Yuxiang Han
As the Internet of Things (IoT) and Sixth Generation (6G) technologies advance rapidly, the cryptographic identification of electronic devices has become a critical issue in information security. Radio frequency fingerprint (RFF)-based specific emitter identification (SEI) has emerged as a prominent physical-layer authentication technique. To enhance the stability and accuracy of multi-target recognition in complex electromagnetic environments, a novel technique for specific emitter identification based on time-frequency similarity contrastive learning (TFSCL) is proposed. In this study, we present a novel pre-training method utilizing a deep complex-valued pyramid network (DCPN) to enhance the extraction and reconstruction of time series and frequency domain sequences. The DCPN enables contrastive learning of signal features in both the temporal and frequency domains, significantly reducing computational complexity and improving pretraining performance. Additionally, we first introduce the Time-Frequency Synchronization Data Added (TFS-DA), a Time-Frequency Hybrid Data Added Technique that employs Gray code to generate random sequences, effectively improving feature representation in both domains. Empirical results demonstrate that the proposed method achieves an accuracy rate of 97.12% on an automatic-dependent surveillance-broadcast (ADS-B) dataset that contains 10 categories with only data labeled 10%. On a LoRa dataset containing 30 categories with only data labeled 10%, the accuracy rate reaches 77.06%.
{"title":"TFSCL: A Novel Time-Frequency Similarity Contrastive Learning Method With Hybrid Augmentation for Robust and Accurate Specific Emitter Identification","authors":"Kaixuan Huang;Yongtao Ma;Jialu Zhu;Yuxiang Han","doi":"10.1109/TIFS.2026.3671097","DOIUrl":"10.1109/TIFS.2026.3671097","url":null,"abstract":"As the Internet of Things (IoT) and Sixth Generation (6G) technologies advance rapidly, the cryptographic identification of electronic devices has become a critical issue in information security. Radio frequency fingerprint (RFF)-based specific emitter identification (SEI) has emerged as a prominent physical-layer authentication technique. To enhance the stability and accuracy of multi-target recognition in complex electromagnetic environments, a novel technique for specific emitter identification based on time-frequency similarity contrastive learning (TFSCL) is proposed. In this study, we present a novel pre-training method utilizing a deep complex-valued pyramid network (DCPN) to enhance the extraction and reconstruction of time series and frequency domain sequences. The DCPN enables contrastive learning of signal features in both the temporal and frequency domains, significantly reducing computational complexity and improving pretraining performance. Additionally, we first introduce the Time-Frequency Synchronization Data Added (TFS-DA), a Time-Frequency Hybrid Data Added Technique that employs Gray code to generate random sequences, effectively improving feature representation in both domains. Empirical results demonstrate that the proposed method achieves an accuracy rate of 97.12% on an automatic-dependent surveillance-broadcast (ADS-B) dataset that contains 10 categories with only data labeled 10%. On a LoRa dataset containing 30 categories with only data labeled 10%, the accuracy rate reaches 77.06%.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2668-2681"},"PeriodicalIF":8.0,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371545","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}
Pub Date : 2026-03-05DOI: 10.1109/tifs.2026.3671078
Xiao Tang, Zhen Ma, Limeng Dong, Yichen Wang, Qinghe Du, Dusit Niyato, Zhu Han
{"title":"Uncertainty-Aware Jamming Mitigation with Active RIS: A Robust Stackelberg Game Approach","authors":"Xiao Tang, Zhen Ma, Limeng Dong, Yichen Wang, Qinghe Du, Dusit Niyato, Zhu Han","doi":"10.1109/tifs.2026.3671078","DOIUrl":"https://doi.org/10.1109/tifs.2026.3671078","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"236 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371514","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}
Pub Date : 2026-03-05DOI: 10.1109/TIFS.2026.3671104
Xian Qin;Xue Yang;Xiaohu Tang
Federated Learning (FL) allows collaborative model training across distributed clients without sharing raw data, thus preserving privacy. However, the system remains vulnerable to privacy leakage from gradient updates and Byzantine attacks from malicious clients. Existing solutions face a critical trade-off among privacy preservation, Byzantine robustness, and computational efficiency. We propose a novel scheme that effectively balances these competing objectives by integrating homomorphic encryption with dimension compression based on the Johnson-Lindenstrauss transformation. Our approach employs a dual-server architecture that enables secure Byzantine defense in the ciphertext domain while dramatically reducing computational overhead through gradient compression. The dimension compression technique preserves the geometric relationships necessary for Byzantine defence while reducing computation complexity from $O(dn)$ to $O(kn)$ cryptographic operations, where $k ll d$ . Extensive experiments across diverse datasets demonstrate that our approach maintains model accuracy comparable to non-private FL while effectively defending against Byzantine clients comprising up to 40% of the network. Our approach also demonstrates substantial improvements in computational and communication efficiency. Experimental evaluation shows that the dimension compression technique achieves $25 times sim 35 times $ reduction in computational overhead and $17 times $ reduction in communication overhead compared to our non-compression version. When compared to state-of-the-art methods like ShieldFL, our approach demonstrates order-of-magnitude improvements in both computational and communication efficiency while maintaining equivalent privacy guarantees and achieving superior Byzantine robustness comparable to FLTrust. These substantial efficiency enhancements make secure FL practical for deployment in large-scale neural networks with millions of parameters.
{"title":"Efficient Byzantine-Robust Privacy-Preserving Federated Learning via Dimension Compression","authors":"Xian Qin;Xue Yang;Xiaohu Tang","doi":"10.1109/TIFS.2026.3671104","DOIUrl":"https://doi.org/10.1109/TIFS.2026.3671104","url":null,"abstract":"Federated Learning (FL) allows collaborative model training across distributed clients without sharing raw data, thus preserving privacy. However, the system remains vulnerable to privacy leakage from gradient updates and Byzantine attacks from malicious clients. Existing solutions face a critical trade-off among privacy preservation, Byzantine robustness, and computational efficiency. We propose a novel scheme that effectively balances these competing objectives by integrating homomorphic encryption with dimension compression based on the Johnson-Lindenstrauss transformation. Our approach employs a dual-server architecture that enables secure Byzantine defense in the ciphertext domain while dramatically reducing computational overhead through gradient compression. The dimension compression technique preserves the geometric relationships necessary for Byzantine defence while reducing computation complexity from <inline-formula> <tex-math>$O(dn)$ </tex-math></inline-formula> to <inline-formula> <tex-math>$O(kn)$ </tex-math></inline-formula> cryptographic operations, where <inline-formula> <tex-math>$k ll d$ </tex-math></inline-formula>. Extensive experiments across diverse datasets demonstrate that our approach maintains model accuracy comparable to non-private FL while effectively defending against Byzantine clients comprising up to 40% of the network. Our approach also demonstrates substantial improvements in computational and communication efficiency. Experimental evaluation shows that the dimension compression technique achieves <inline-formula> <tex-math>$25 times sim 35 times $ </tex-math></inline-formula> reduction in computational overhead and <inline-formula> <tex-math>$17 times $ </tex-math></inline-formula> reduction in communication overhead compared to our non-compression version. When compared to state-of-the-art methods like ShieldFL, our approach demonstrates order-of-magnitude improvements in both computational and communication efficiency while maintaining equivalent privacy guarantees and achieving superior Byzantine robustness comparable to FLTrust. These substantial efficiency enhancements make secure FL practical for deployment in large-scale neural networks with millions of parameters.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"21 ","pages":"2596-2609"},"PeriodicalIF":8.0,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440573","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}
Pub Date : 2026-03-05DOI: 10.1109/tifs.2026.3671126
Zhenya Ma, Yongheng Deng, Ziqing Qiao, Quan Zhang, Chijin Zhou, Fan Wu, Yaoxue Zhang, Ju Ren
{"title":"A Fine-Tuning Data Recovery Attack on Generative Language Models via Backdooring","authors":"Zhenya Ma, Yongheng Deng, Ziqing Qiao, Quan Zhang, Chijin Zhou, Fan Wu, Yaoxue Zhang, Ju Ren","doi":"10.1109/tifs.2026.3671126","DOIUrl":"https://doi.org/10.1109/tifs.2026.3671126","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"73 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147371504","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}
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