IEEE Transactions on Information Forensics and Security最新文献_第4页

Identity-Based Chameleon Hashes in the Standard Model for Mobile Devices

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-17 DOI: 10.1109/tifs.2025.3552196

Cong Li, Xiaoyu Jiao, Xinyu Feng, Anyang Hu, Qingni Shen, Zhonghai Wu

引用次数: 0

Parallel PAM for Secure Transmission

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-17 DOI: 10.1109/TIFS.2025.3552035

Hongliang He;Nengcheng Chen

引用次数: 0

An Adaptive Domain-Incremental Framework With Knowledge Replay and Domain Alignment for Specific Emitter Identification

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-17 DOI: 10.1109/TIFS.2025.3552034

Xiaoyu Shen;Tao Zhang;Hao Wu;Xiaoqiang Qiao;Yihang Du;Guan Gui

{"title":"An Adaptive Domain-Incremental Framework With Knowledge Replay and Domain Alignment for Specific Emitter Identification","authors":"Xiaoyu Shen;Tao Zhang;Hao Wu;Xiaoqiang Qiao;Yihang Du;Guan Gui","doi":"10.1109/TIFS.2025.3552034","DOIUrl":"10.1109/TIFS.2025.3552034","url":null,"abstract":"Specific Emitter Identification (SEI) is crucial for ensuring the security of physical layer communication. However, signal characteristics can be affected by various factors such as environmental and equipment variations. An effective SEI system must continuously learn and adapt to these changes to maintain accurate signal recognition. This study proposes an advanced domain incremental learning (DIL) framework for SEI, named Adaptive Domain-Incremental Learning with Knowledge Replay and Domain Alignment (ADIRA). ADIRA employs knowledge replay and distillation strategies, along with adaptive coefficients, to balance the model’s performance in recognizing signals across both new and old domains. To address the variations in signal data feature distributions across different domains, we introduce a domain alignment strategy based on adversarial training. This approach integrates embedding distillation loss with supervised contrastive loss, significantly enhancing the model’s adaptability to domain changes. Experimental results on two benchmark datasets demonstrate that ADIRA achieves performance only 0.42% and 1.71% lower than joint training, with replay samples constituting just 1.1% and 1.5% of the training set, effectively mitigating catastrophic forgetting.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3519-3533"},"PeriodicalIF":6.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640675","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

Theory and Applications of Sequentially Threshold Public-Key Cryptography: Practical Private Key Safeguarding and Secure Use for Individual Users

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-17 DOI: 10.1109/TIFS.2025.3552202

Jie Zhang;Futai Zhang;Xinyi Huang

{"title":"Theory and Applications of Sequentially Threshold Public-Key Cryptography: Practical Private Key Safeguarding and Secure Use for Individual Users","authors":"Jie Zhang;Futai Zhang;Xinyi Huang","doi":"10.1109/TIFS.2025.3552202","DOIUrl":"10.1109/TIFS.2025.3552202","url":null,"abstract":"Motivated by the needs of power distribution as well as private key protection, the theory and implementation techniques of threshold public-key cryptography (PKC) have been being developed for a long time. However, researches in this field mainly focus on the needs and constraints in distributed environments which consist of nodes with computing capabilities and connected via peer-to-peer and broadcasting communication channels. The resulting schemes are theoretically helpful for private key security but inconvenient for individual users as their implementation requires distributed computing and networking system with broadcasting channels. To address the private key security issue of PKC schemes for individual users, this paper proposes the concept and general construction of sequentially threshold PKC under a communication model consisting of a computing device and several offline storages where broadcasting channels are not required. To illustrate the new paradigm, we design and realize a sequentially threshold Schnorr signature scheme <monospace>STSS</monospace>. The security proofs for <monospace>STSS</monospace> indicate its effectiveness of achieving unforeability under traditional attacks as well as security incidents caused by human faults and system failures. The experiments on FIPS recommended curves P-256, P-384, and P-521 show that <monospace>STSS</monospace> is comparable with the original Schnorr scheme in terms of time consumed for generating a signature. The construction of sequentially threshold ElGamal decrtyption scheme is also presented. Finally, we illustrate the application of <monospace>STSS</monospace> in the Blockchain ecosystem.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3220-3233"},"PeriodicalIF":6.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640768","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

Boosting Adversarial Transferability via Relative Feature Importance-Aware Attacks

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-17 DOI: 10.1109/TIFS.2025.3552030

Jian-Wei Li;Wen-Ze Shao;Yu-Bao Sun;Li-Qian Wang;Qi Ge;Liang Xiao

{"title":"Boosting Adversarial Transferability via Relative Feature Importance-Aware Attacks","authors":"Jian-Wei Li;Wen-Ze Shao;Yu-Bao Sun;Li-Qian Wang;Qi Ge;Liang Xiao","doi":"10.1109/TIFS.2025.3552030","DOIUrl":"10.1109/TIFS.2025.3552030","url":null,"abstract":"Modern deep neural networks are known highly vulnerable to adversarial examples. As a pioneering work, the fast gradient sign method (FGSM) is proved more transferable in black-box attacks than its multi-small-step extension, i.e., iterative-FGSM, particularly being restricted by a limited number of iterations. This paper revisits their early, representative successor MI-FGSM as a baseline, i.e., iterative-FGSM with momentum, and introduces an innovative boosting idea different from either FGSM-inspired algorithms or other mainstream methods. For one thing, during gradient backpropogation of MI-FGSM, the proposed approach merely requires amending the chain rule with respect to adversarial images using the counterpart original images. For another, a credible analysis has revealed that such a naively boosted MI-FGSM essentially performs a special kind of intermediate-layer attacks. In specific, the notable finding in the paper is a new principle of adversarial transferability guided by the relative feature importance, emphasizing the significance of semantically non-critical information for the first time in the literature, although originally thought to be weak in large. Experimental results on various leading victim models, both undefended and defended, demonstrate that the new approach incorporating robust gradients has indeed attained stronger adversarial transferability than state-of-the-art works. The code is available at:<uri>https://github.com/ljwooo/RFIA-main</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3489-3504"},"PeriodicalIF":6.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640508","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

Sensitivity-Aware Personalized Differential Privacy Guarantees for Online Social Networks

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-14 DOI: 10.1109/TIFS.2025.3551642

Jiajun Chen;Chunqiang Hu;Weihong Sheng;Tao Xiang;Pengfei Hu;Jiguo Yu

{"title":"Sensitivity-Aware Personalized Differential Privacy Guarantees for Online Social Networks","authors":"Jiajun Chen;Chunqiang Hu;Weihong Sheng;Tao Xiang;Pengfei Hu;Jiguo Yu","doi":"10.1109/TIFS.2025.3551642","DOIUrl":"10.1109/TIFS.2025.3551642","url":null,"abstract":"With the prevalence of online social networks (OSNs), much personal information is collected and maintained by trusted service providers for third-party queries and analyses. Existing works regarding differentially private social network data publication overlook the fact that different users exhibit distinct privacy preferences or sensitivity inclinations. Neglecting these individual nuances may lead to privacy mechanisms that are overly conservative or inadequately protective. Furthermore, the injection of excessive noise into OSN data perceived by users as non-personal or less sensitive can incur additional privacy costs, resulting in lower service quality. This paper introduces a fine-grained, sensitivity-aware personalized edge differential privacy model (SPEDP) for OSNs. Specifically, SPEDP enables each OSN user to individually define the sensitivity level of their social connections, facilitating user-friendly personalized privacy settings. We design a privacy-aware mechanism that operates within a trusted service provider, capable of establishing privacy protection levels based on user-perceived sensitivity settings. Additionally, we propose a sensitivity-aware sampling mechanism to implement SPEDP. To further optimize the privacy mechanism, we explore a privacy threshold optimization strategy aimed at minimizing privacy budget waste. Finally, the personalized privacy protections and utility improvements achieved by the SPEDP mechanism are rigorously validated through theoretical analysis and comprehensive comparative experiments on benchmark datasets.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3116-3130"},"PeriodicalIF":6.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631302","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

Uncoordinated Syntactic Privacy: A New Composable Metric for Multiple, Independent Data Publishing 非协调语法隐私：用于多个独立数据发布的可组合新指标

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-14 DOI: 10.1109/TIFS.2025.3551645

Adrián Tobar Nicolau;Javier Parra-Arnau;Jordi Forné;Vicenç Torra

{"title":"Uncoordinated Syntactic Privacy: A New Composable Metric for Multiple, Independent Data Publishing","authors":"Adrián Tobar Nicolau;Javier Parra-Arnau;Jordi Forné;Vicenç Torra","doi":"10.1109/TIFS.2025.3551645","DOIUrl":"10.1109/TIFS.2025.3551645","url":null,"abstract":"A privacy model is a privacy condition, dependent on a parameter, that guarantees an upper bound on the risk of reidentification disclosure and maybe also on the risk of attribute disclosure by an adversary. A privacy model is composable if the privacy guarantees of the model are preserved, possibly to a limited extent, after repeated independent application of the privacy model. From the opposite perspective, a privacy model is not composable if multiple independent data releases, each of them satisfying the requirements of the privacy model, may result in a privacy breach. Current privacy models are broadly classified into syntactic ones (such as k-anonymity and l-diversity) and semantic ones, which essentially refer to <inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula>-differential privacy (e-DP) and variations thereof. While e-DP and its variants offer strong composability properties, syntactic notions are not composable unless data releases are conducted by a single, centralized data holder that uses specialized notions such as m-invariance and <inline-formula> <tex-math>$tau $ </tex-math></inline-formula>-safety. In this work, we propose m-uncoordinated-syntactic-privacy (m-USP), the first syntactic notion with composability properties for the independent publication of nondisjoint data, in other words, without a centralized data holder. Theoretical results are formally proven, and experimental results demonstrate that the risk to individuals does not increase significantly, in contrast to non-composable methods, that are susceptible to attribute disclosure. In most cases, the utility degradation caused by the extra protection is less than 5% and decreases as the value of m increases.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3362-3373"},"PeriodicalIF":6.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10926580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Reentrancy Vulnerabilities for Solidity Smart Contracts with Contract Standards-Based Rules 利用基于合约标准的规则检测 Solidity 智能合约的重入漏洞

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-14 DOI: 10.1109/tifs.2025.3551535

Jie Cai, Jiachi Chen, Tao Zhang, Xiapu Luo, Xiaobing Sun, Bin Li

引用次数: 0

Dual-cohesion Metric Learning for Few-shot Hand-based Multimodal Recognition 基于少量手部图像的多模态识别的双聚合度量学习

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-14 DOI: 10.1109/tifs.2025.3551646

Shuyi Li, Bob Zhang, Qinghua Hu

引用次数: 0

Vulnerabilities of NSPFL: Privacy-preserving Federated Learning with Data Integrity Auditing