Pub Date : 2025-02-10DOI: 10.1109/tifs.2025.3540312
Boan Yu, Jun Zhao, Kai Zhang, Junqing Gong, Haifeng Qian
{"title":"Lightweight and Dynamic Privacy-Preserving Federated Learning via Functional Encryption","authors":"Boan Yu, Jun Zhao, Kai Zhang, Junqing Gong, Haifeng Qian","doi":"10.1109/tifs.2025.3540312","DOIUrl":"https://doi.org/10.1109/tifs.2025.3540312","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"14 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385647","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 : 2025-02-10DOI: 10.1109/tifs.2025.3539964
Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh, Alexandre Graell i Amat
{"title":"FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation","authors":"Marvin Xhemrishi, Johan Östman, Antonia Wachter-Zeh, Alexandre Graell i Amat","doi":"10.1109/tifs.2025.3539964","DOIUrl":"https://doi.org/10.1109/tifs.2025.3539964","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"33 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385649","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 : 2025-02-10DOI: 10.1109/tifs.2025.3540357
Ya-Ting Yang, Tao Zhang, Quanyan Zhu
{"title":"Herd Accountability of Privacy-Preserving Algorithms: A Stackelberg Game Approach","authors":"Ya-Ting Yang, Tao Zhang, Quanyan Zhu","doi":"10.1109/tifs.2025.3540357","DOIUrl":"https://doi.org/10.1109/tifs.2025.3540357","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"208 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385648","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 : 2025-02-10DOI: 10.1109/TIFS.2025.3540606
Fucai Luo;Xingfu Yan;Haining Yang;Xiaofan Zheng
Public-key Encryption with Keyword Search (PEKS) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform (on-demand) keyword searches over encrypted data for data users. Existing PEKS schemes are limited to precise or fuzzy keyword searches, creating a gap given the widespread use of wildcards for rapid searches in real-world applications. To address this issue, several wildcard keyword search schemes have been proposed to support wildcard searches in the public-key setting. However, these schemes suffer from inefficiency and/or inflexibility. Worse yet, they are all vulnerable to (insider) keyword guessing attacks (KGA), which is highly effective when the keyword space is polynomial in size. To address these vulnerabilities, this paper first proposes a new wildcard keyword search scheme called Public-key Encryption with Wildcard Search (PEWS), which is built based on the standard Decisional Diffie-Hellman (DDH) assumption. The complexity of all algorithms in PEWS increases linearly with the keyword length, while remaining almost constant or even decreasing linearly with the number of wildcards. To resist against (insider) KGA, we further extend PEWS into the first Public-key Authenticated Encryption with Wildcard Search (PAEWS) scheme. Our PEWS and PAEWS schemes are highly flexible, supporting searches for any number of wildcards positioned anywhere within the keyword. We conduct a comprehensive performance evaluation of our PEWS and PAEWS, while also comparing PEWS with the state-of-the-art scheme in the public-key setting. The experimental results demonstrate that both PEWS and PAEWS are efficient and practical, and the experimental comparisons illustrate that PEWS achieves approximately $2 times $ faster computation and reduces communication by at least 50%.
{"title":"PAEWS: Public-Key Authenticated Encryption With Wildcard Search Over Outsourced Encrypted Data","authors":"Fucai Luo;Xingfu Yan;Haining Yang;Xiaofan Zheng","doi":"10.1109/TIFS.2025.3540606","DOIUrl":"10.1109/TIFS.2025.3540606","url":null,"abstract":"Public-key Encryption with Keyword Search (PEKS) is a promising cryptographic mechanism that enables a semi-trusted cloud server to perform (on-demand) keyword searches over encrypted data for data users. Existing PEKS schemes are limited to precise or fuzzy keyword searches, creating a gap given the widespread use of wildcards for rapid searches in real-world applications. To address this issue, several wildcard keyword search schemes have been proposed to support wildcard searches in the public-key setting. However, these schemes suffer from inefficiency and/or inflexibility. Worse yet, they are all vulnerable to (insider) keyword guessing attacks (KGA), which is highly effective when the keyword space is polynomial in size. To address these vulnerabilities, this paper first proposes a new wildcard keyword search scheme called Public-key Encryption with Wildcard Search (PEWS), which is built based on the standard Decisional Diffie-Hellman (DDH) assumption. The complexity of all algorithms in PEWS increases linearly with the keyword length, while remaining almost constant or even decreasing linearly with the number of wildcards. To resist against (insider) KGA, we further extend PEWS into the first Public-key Authenticated Encryption with Wildcard Search (PAEWS) scheme. Our PEWS and PAEWS schemes are highly flexible, supporting searches for any number of wildcards positioned anywhere within the keyword. We conduct a comprehensive performance evaluation of our PEWS and PAEWS, while also comparing PEWS with the state-of-the-art scheme in the public-key setting. The experimental results demonstrate that both PEWS and PAEWS are efficient and practical, and the experimental comparisons illustrate that PEWS achieves approximately <inline-formula> <tex-math>$2 times $ </tex-math></inline-formula> faster computation and reduces communication by at least 50%.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2212-2223"},"PeriodicalIF":6.3,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385651","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":"On the Impact of Warden Collusion on Covert Communication in Wireless Networks","authors":"Shuangrui Zhao, Jia Liu, Yulong Shen, Xiaohong Jiang, Tarik Taleb, Norio Shiratori","doi":"10.1109/tifs.2025.3540575","DOIUrl":"https://doi.org/10.1109/tifs.2025.3540575","url":null,"abstract":"","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"55 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143385646","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 : 2025-02-07DOI: 10.1109/TIFS.2025.3539943
Mingping Qi;Chi Chen
Secure Shell (SSH) is a robust cryptographic network protocol designed to establish a secure and encrypted connection over potentially insecure networks, which is typically used for remote login and command-line execution on remote systems. As its core foundation, SSH Transport Layer Protocol relies on the classic (Elliptic Curve) Diffie-Hellman ((EC)DH) key exchange protocol to achieve session key establishment, whose security is essentially based on the (EC) discrete logarithm problem ((EC)DLP). However, the classic (EC)DLP problem could be broken using sufficiently powerful quantum computers when it comes to the post-quantum era, which implies that the traditional SSH protocol will be insecure against the quantum computer attacks. To this end, this paper presents a hybrid post-quantum alternative for the SSH Transport Layer Protocol, called as HPQKE, which combines the supersingular isogeny based post-quantum CSIDH (Commutative Supersingular Isogeny Diffie-Hellman) and the classic ECDH key exchange protocols together. The security of each individual key exchange protocol within the presented HPQKE operates independently, ensuring that the overall security of the HPQKE remains at least as robust as the most secure key exchange protocol employed during its key exchange processes. Moreover, we formally prove that if the used MAC scheme is EUF-CMA secure, then (1) HPQKE is a post-quantum secure key exchange protocol if the CSIDH based Gap Computational Diffie-Hellman (CSI-GDH) security assumption holds, and (2) HPQKE is a classically secure key exchange protocol if the traditional GDH security assumption holds. In addition, we provide a prototype implementation for the HPQKE in a real network environment, and the corresponding experimental results intuitively demonstrate its practical feasibility.
{"title":"HPQKE: Hybrid Post-Quantum Key Exchange Protocol for SSH Transport Layer From CSIDH","authors":"Mingping Qi;Chi Chen","doi":"10.1109/TIFS.2025.3539943","DOIUrl":"10.1109/TIFS.2025.3539943","url":null,"abstract":"Secure Shell (SSH) is a robust cryptographic network protocol designed to establish a secure and encrypted connection over potentially insecure networks, which is typically used for remote login and command-line execution on remote systems. As its core foundation, SSH Transport Layer Protocol relies on the classic (Elliptic Curve) Diffie-Hellman ((EC)DH) key exchange protocol to achieve session key establishment, whose security is essentially based on the (EC) discrete logarithm problem ((EC)DLP). However, the classic (EC)DLP problem could be broken using sufficiently powerful quantum computers when it comes to the post-quantum era, which implies that the traditional SSH protocol will be insecure against the quantum computer attacks. To this end, this paper presents a hybrid post-quantum alternative for the SSH Transport Layer Protocol, called as HPQKE, which combines the supersingular isogeny based post-quantum CSIDH (Commutative Supersingular Isogeny Diffie-Hellman) and the classic ECDH key exchange protocols together. The security of each individual key exchange protocol within the presented HPQKE operates independently, ensuring that the overall security of the HPQKE remains at least as robust as the most secure key exchange protocol employed during its key exchange processes. Moreover, we formally prove that if the used MAC scheme is EUF-CMA secure, then (1) HPQKE is a post-quantum secure key exchange protocol if the CSIDH based Gap Computational Diffie-Hellman (CSI-GDH) security assumption holds, and (2) HPQKE is a classically secure key exchange protocol if the traditional GDH security assumption holds. In addition, we provide a prototype implementation for the HPQKE in a real network environment, and the corresponding experimental results intuitively demonstrate its practical feasibility.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2122-2131"},"PeriodicalIF":6.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367386","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 : 2025-02-07DOI: 10.1109/TIFS.2025.3539087
Zhuoran Ma;Xinyi Huang;Zhuzhu Wang;Zhan Qin;Xiangyu Wang;Jianfeng Ma
FL is vulnerable to model poisoning attacks due to the invisibility of local data and the decentralized nature of FL training. The adversary attempts to maliciously manipulate local model gradients to compromise the global model (i.e., victim model). Commonly-studied model poisoning attacks heavily depend on accessing additional knowledge, such as local data and the aggregation algorithm from the victim model, which easily encounter practical obstacles due to limited adversarial knowledge. In this paper, we first reveal that aggregated gradients in FL can serve as an attack carrier, exposing the latent knowledge of the victim model. In particular, we propose a data-free model poisoning attack named FedGhost, which aims to redirect the training objective of FL towards the adversary’s objective without any auxiliary information. In FedGhost, we design a black-box adaptive optimization algorithm to dynamically adjust the perturbation factor for malicious gradients, maximizing the poisoning impact of FL. Experimental results on five datasets in IID and Non-IID FL settings demonstrate that FedGhost achieves the highest attack success rate, outperforming other state-of-the-art model poisoning attacks by more than $10%-60%$ .
{"title":"FedGhost: Data-Free Model Poisoning Enhancement in Federated Learning","authors":"Zhuoran Ma;Xinyi Huang;Zhuzhu Wang;Zhan Qin;Xiangyu Wang;Jianfeng Ma","doi":"10.1109/TIFS.2025.3539087","DOIUrl":"10.1109/TIFS.2025.3539087","url":null,"abstract":"FL is vulnerable to model poisoning attacks due to the invisibility of local data and the decentralized nature of FL training. The adversary attempts to maliciously manipulate local model gradients to compromise the global model (i.e., victim model). Commonly-studied model poisoning attacks heavily depend on accessing additional knowledge, such as local data and the aggregation algorithm from the victim model, which easily encounter practical obstacles due to limited adversarial knowledge. In this paper, we first reveal that aggregated gradients in FL can serve as an attack carrier, exposing the latent knowledge of the victim model. In particular, we propose a data-free model poisoning attack named FedGhost, which aims to redirect the training objective of FL towards the adversary’s objective without any auxiliary information. In FedGhost, we design a black-box adaptive optimization algorithm to dynamically adjust the perturbation factor for malicious gradients, maximizing the poisoning impact of FL. Experimental results on five datasets in IID and Non-IID FL settings demonstrate that FedGhost achieves the highest attack success rate, outperforming other state-of-the-art model poisoning attacks by more than <inline-formula> <tex-math>$10%-60%$ </tex-math></inline-formula>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2096-2108"},"PeriodicalIF":6.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367387","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}
Password-based threshold single-sign-on authentication (PbTA) allows multiple identity servers to in a threshold manner authenticate a user and issue a token, with which the user accesses relevant services. We analyze existing PbTA schemes and reveal a potential threat: vulnerability against perpetual credential leakage, in which “perpetual” adversaries could perpetually attempt to compromise long-lived credential databases maintained by identity servers. Compromising a threshold number of credential databases enables the adversaries to launch offline dictionary guessing attacks (DGA) or illegally obtain users’ tokens. To address these issues, we first propose a basic device-enhanced PbTA scheme (DE-PbTA), where an auxiliary device collaborates with identity servers in hardening a user’s password during authentication, such that perpetual adversaries cannot learn the password from compromised credentials via offline DGA. Using the hardened password, a private key can be derived to decrypt ciphertexts from identity servers for token construction, which protects the user’s tokens against perpetual adversaries. Then, we extend basic DE-PbTA to support dynamic usage of multiple devices, where a user can actively choose $t^{prime } $ devices out of $n^{prime } $ for authentication. Provable security and high efficiency of the basic/enhanced DE-PbTA scheme are demonstrated by comprehensive analysis and experimental evaluations.
{"title":"Device-Enhanced Password-Based Threshold Single-Sign-On Authentication","authors":"Changsong Jiang;Chunxiang Xu;Guomin Yang;Zhao Zhang;Jie Chen","doi":"10.1109/TIFS.2025.3539955","DOIUrl":"10.1109/TIFS.2025.3539955","url":null,"abstract":"Password-based threshold single-sign-on authentication (PbTA) allows multiple identity servers to in a threshold manner authenticate a user and issue a token, with which the user accesses relevant services. We analyze existing PbTA schemes and reveal a potential threat: vulnerability against perpetual credential leakage, in which “perpetual” adversaries could perpetually attempt to compromise long-lived credential databases maintained by identity servers. Compromising a threshold number of credential databases enables the adversaries to launch offline dictionary guessing attacks (DGA) or illegally obtain users’ tokens. To address these issues, we first propose a basic device-enhanced PbTA scheme (DE-PbTA), where an auxiliary device collaborates with identity servers in hardening a user’s password during authentication, such that perpetual adversaries cannot learn the password from compromised credentials via offline DGA. Using the hardened password, a private key can be derived to decrypt ciphertexts from identity servers for token construction, which protects the user’s tokens against perpetual adversaries. Then, we extend basic DE-PbTA to support dynamic usage of multiple devices, where a user can actively choose <inline-formula> <tex-math>$t^{prime } $ </tex-math></inline-formula> devices out of <inline-formula> <tex-math>$n^{prime } $ </tex-math></inline-formula> for authentication. Provable security and high efficiency of the basic/enhanced DE-PbTA scheme are demonstrated by comprehensive analysis and experimental evaluations.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2006-2021"},"PeriodicalIF":6.3,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143367384","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 : 2025-02-06DOI: 10.1109/TIFS.2025.3539527
Sisung Liu;Jeong Gyu Park;Hyeongsik Kim;Je Hyeong Hong
File fragment classification is a crucial task in digital forensics and cybersecurity, and has recently achieved significant improvement through the deployment of convolutional neural networks (CNNs) compared to traditional handcrafted feature-based methods. However, CNN-based models exhibit inherent biases that can limit their effectiveness for larger datasets. To address this limitation, we propose the Cross-Attention Multi-Scale Performer (XMP) model, which integrates the attention mechanisms of transformer encoders with the feature extraction capabilities of CNNs. Compared to our conference work, we additionally introduce a new Gaussian Bit-Flip (GBFlip) method for binary data augmentation, largely inspired by bit flipping errors in digital system, improving the model performance. Furthermore, we incorporate a fine-tuning approach and demonstrate XMP adapts more effectively to diverse datasets than other CNN-based competitors without extensive hyperparameter tuning. Our experimental results on two public file fragment classification datasets show XMP surpassing other CNN-based and RCNN-based models, achieving state-of-the-art performance in file fragment classification both with and without fine-tuning. Our code is available at https://github.com/DominicoRyu/XMP_TIFS.
{"title":"A Cross-Attention Multi-Scale Performer With Gaussian Bit-Flips for File Fragment Classification","authors":"Sisung Liu;Jeong Gyu Park;Hyeongsik Kim;Je Hyeong Hong","doi":"10.1109/TIFS.2025.3539527","DOIUrl":"10.1109/TIFS.2025.3539527","url":null,"abstract":"File fragment classification is a crucial task in digital forensics and cybersecurity, and has recently achieved significant improvement through the deployment of convolutional neural networks (CNNs) compared to traditional handcrafted feature-based methods. However, CNN-based models exhibit inherent biases that can limit their effectiveness for larger datasets. To address this limitation, we propose the Cross-Attention Multi-Scale Performer (XMP) model, which integrates the attention mechanisms of transformer encoders with the feature extraction capabilities of CNNs. Compared to our conference work, we additionally introduce a new Gaussian Bit-Flip (GBFlip) method for binary data augmentation, largely inspired by bit flipping errors in digital system, improving the model performance. Furthermore, we incorporate a fine-tuning approach and demonstrate XMP adapts more effectively to diverse datasets than other CNN-based competitors without extensive hyperparameter tuning. Our experimental results on two public file fragment classification datasets show XMP surpassing other CNN-based and RCNN-based models, achieving state-of-the-art performance in file fragment classification both with and without fine-tuning. Our code is available at <uri>https://github.com/DominicoRyu/XMP_TIFS</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2109-2121"},"PeriodicalIF":6.3,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258742","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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