IEEE Transactions on Information Forensics and Security最新文献_第6页

Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/tifs.2025.3550079

Yihao Huang, Xin Luo, Qing Guo, Felix Juefei-Xu, Xiaojun Jia, Weikai Miao, Geguang Pu, Yang Liu

引用次数: 0

COKV: Key-Value Data Collection With Condensed Local Differential Privacy

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/TIFS.2025.3550064

Junpeng Zhang;Hui Zhu;Jiaqi Zhao;Rongxing Lu;Yandong Zheng;Jiezhen Tang;Hui Li

{"title":"COKV: Key-Value Data Collection With Condensed Local Differential Privacy","authors":"Junpeng Zhang;Hui Zhu;Jiaqi Zhao;Rongxing Lu;Yandong Zheng;Jiezhen Tang;Hui Li","doi":"10.1109/TIFS.2025.3550064","DOIUrl":"10.1109/TIFS.2025.3550064","url":null,"abstract":"Local differential privacy (LDP) provides lightweight and provable privacy protection and has wide applications in private data collection. Key-value data, as a popular NoSQL structure, requires simultaneous frequency and mean estimations of each key, which poses a challenge to traditional LDP-based collection methods. Despite many schemes proposed for the privacy protection of key-value data, they inadequately solve the condensed perturbation for keys and the advanced combination of privacy budgets, leading to suboptimal estimation accuracy. To address this issue, we propose an efficient key-value collection scheme (COKV) with tight privacy budget composition. In our scheme, we first design a padding and sampling protocol for key-value data to avoid privacy budget splitting. Second, to enhance the utility of key perturbation, we design a key perturbation primitive and optimize the perturbation range to improve computational efficiency. After that, we propose a key-value association perturbation algorithm whose value perturbation strategy guarantees the output expectation equals the original value. Finally, we demonstrate that through a tight privacy budget composition, COKV can provide higher data utility under the same privacy level. Theoretical analysis shows that COKV possesses lower frequency and mean estimations variance. Extensive experiments on both synthetic and real-world datasets also indicate that COKV outperforms the current state-of-the-art methods for secure key-value data collection.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3260-3273"},"PeriodicalIF":6.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598748","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

Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/TIFS.2025.3550054

Yongliang Xu;Hang Cheng;Jiguo Li;Ximeng Liu;Xinpeng Zhang;Meiqing Wang

{"title":"Lightweight Multi-User Public-Key Authenticated Encryption With Keyword Search","authors":"Yongliang Xu;Hang Cheng;Jiguo Li;Ximeng Liu;Xinpeng Zhang;Meiqing Wang","doi":"10.1109/TIFS.2025.3550054","DOIUrl":"10.1109/TIFS.2025.3550054","url":null,"abstract":"Data confidentiality, a fundamental security element for dependable cloud storage, has been drawing widespread concern. Public-key encryption with keyword search (PEKS) has emerged as a promising approach for privacy protection while enabling efficient retrieval of encrypted data. One of the typical applications of PEKS is searching sensitive electronic medical records (EMR) in healthcare clouds. However, many traditional countermeasures fall short of balancing privacy protection with search efficiency, and they often fail to support multi-user EMR sharing. To resolve these challenges, we propose a novel lightweight multi-user public-key authenticated encryption scheme with keyword search (LM-PAEKS). Our design effectively counters the inside keyword guessing attack (IKGA) while maintaining the sizes of ciphertext and trapdoor constant in multi-user scenarios. The novelty of our approach relies on introducing a dedicated receiver server that skillfully transforms the complex many-to-many relationship between senders and receivers into a streamlined one-to-one relationship. This transformation prevents the sizes of ciphertext and trapdoor from scaling linearly with the number of participants. Our approach ensures ciphertext indistinguishability and trapdoor privacy while avoiding bilinear pairing operations on the client side. Comparative performance analysis demonstrates that LM-PAEKS features significant computational efficiency while meeting higher security requirements, positioning it as a robust alternative to existing solutions.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3234-3246"},"PeriodicalIF":6.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598859","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

Proofs of Retrievability with Public Verifiability from Lattices

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/tifs.2025.3542248

Miaomiao Tian, Chuang Gao, Long Chen, Hong Zhong, Jie Chen

引用次数: 0

TLARDA: Threshold Label-Aggregating Remote Data Auditing in Decentralized Environment

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/TIFS.2025.3550067

Kuan Liu;Jianting Ning;Pengfei Wu;Shengmin Xu;Rongmao Chen

{"title":"TLARDA: Threshold Label-Aggregating Remote Data Auditing in Decentralized Environment","authors":"Kuan Liu;Jianting Ning;Pengfei Wu;Shengmin Xu;Rongmao Chen","doi":"10.1109/TIFS.2025.3550067","DOIUrl":"10.1109/TIFS.2025.3550067","url":null,"abstract":"Remote data integrity auditing enables a client to efficiently ensure the integrity of entire data stored in untrusted servers via auditing. Yet, existing solutions generally emphasize on various metrics (such as minimal storage, fast update, metadata privacy), but not audit performance (e.g., low audit time, small proof size). To this end, a label-aggregating remote data integrity auditing scheme (LARDA) was proposed in ESORICS ’22, which is the state-of-the-art work in terms of proof size and storage cost. However, LARDA needs a trusted third party (TTP) for performing data auditing for all data owners, which introduces a single point of failure since the audit process routinely needs to interact with the TTP. To address this issue, we introduce a new concept called threshold label-aggregating data auditing and propose two novel schemes. Our first solution is based on Pedersen secret sharing technique, which can significantly alleviate the key escrow problem of LARDA. Our second solution is an efficient batch verifying scheme for multiple TTP’s secret key shares, utilizing the KZG (Kate, Zaverucha and Goldberg) secret sharing technique. This scheme can maintain the size of commitment for TTP’s secret key constant rather than a linear factor with the number of TTPs. We conduct comprehensive experiments to demonstrate the scalability of our schemes. In particular, our second scheme improves the verification time for TTP’s secret key shares in constant, only requiring two pairings and one exponentiation in group with an average of 7.39 ms, regardless of the number of TTPs increasing. For our first scheme, the verification procedure requires <inline-formula> <tex-math>$2t$ </tex-math></inline-formula> exponentiations in group (where t is the threshold value), ranging from 2.37 ms (<inline-formula> <tex-math>$t = 2$ </tex-math></inline-formula>) to 26.85 ms (<inline-formula> <tex-math>$t = 35$ </tex-math></inline-formula>).","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3146-3160"},"PeriodicalIF":6.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599000","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

Constant-Modulus Secure Analog Beamforming for an IRS-Assisted Communication System With Large-Scale Antenna Array

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/TIFS.2025.3550053

Weijie Xiong;Jingran Lin;Zhiling Xiao;Qiang Li

{"title":"Constant-Modulus Secure Analog Beamforming for an IRS-Assisted Communication System With Large-Scale Antenna Array","authors":"Weijie Xiong;Jingran Lin;Zhiling Xiao;Qiang Li","doi":"10.1109/TIFS.2025.3550053","DOIUrl":"10.1109/TIFS.2025.3550053","url":null,"abstract":"Physical layer security (PLS) is an important technology in wireless communication systems to safeguard communication privacy and security between transmitters and legitimate users. The integration of large-scale antenna arrays (LSAA) and intelligent reflecting surfaces (IRS) has emerged as a promising approach to enhance PLS. However, LSAA requires a dedicated radio frequency (RF) chain for each antenna element, and IRS comprises hundreds of reflecting micro-antennas, leading to increased hardware costs and power consumption. To address this, cost-effective solutions like constant modulus analog beamforming (CMAB) have gained attention. This paper investigates PLS in IRS-assisted communication systems with a focus on jointly designing the CMAB at the transmitter and phase shifts at the IRS to maximize the secrecy rate. The resulting secrecy rate maximization (SRM) problem is non-convex. To solve the problem efficiently, we propose two algorithms: 1) the time-efficient Dinkelbach-BSUM algorithm, which reformulates the fractional problem into a series of quadratic programs using the Dinkelbach method and solves them via block successive upper-bound minimization (BSUM), and 2) the product manifold conjugate gradient descent (PMCGD) algorithm, which provides a better solution at the cost of slightly higher computational time by transforming the problem into an unconstrained optimization on a Riemannian product manifold and solving it using the conjugate gradient descent (CGD) algorithm. Simulation results validate the effectiveness of the proposed algorithms and highlight their distinct advantages.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2957-2969"},"PeriodicalIF":6.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598860","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

Revisiting the Masking Strategy: A Side-Channel Attack on CRYSTALS-Kyber

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/TIFS.2025.3550061

Jianfeng Du;Zhu Wang;Aimin Yu

引用次数: 0

Few-Shot Specific Emitter Identification: A Knowledge, Data, and Model-Driven Fusion Framework

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/TIFS.2025.3550080

Minhong Sun;Jiazhong Teng;Xinyuan Liu;Wei Wang;Xingru Huang

{"title":"Few-Shot Specific Emitter Identification: A Knowledge, Data, and Model-Driven Fusion Framework","authors":"Minhong Sun;Jiazhong Teng;Xinyuan Liu;Wei Wang;Xingru Huang","doi":"10.1109/TIFS.2025.3550080","DOIUrl":"10.1109/TIFS.2025.3550080","url":null,"abstract":"In the Industrial Internet of Things (IIoT) context, ensuring secure communication is essential. Specific Emitter Identification (SEI), which leverages subtle differences in radio frequency signals to identify distinct emitters, is key to enhancing communication security. However, traditional SEI methods often rely on large labeled datasets and complex signal processing techniques, which limit their practical applicability due to data acquisition challenges and inefficiency. To address these limitations, we propose a novel Few-shot Specific Emitter Identification (FS-SEI) approach named KDM. This method fuses deep learning with multi-modal data processing, utilizing a hybrid neural network architecture that combines handcrafted features, self-supervised learning, and few-shot learning techniques. Our framework improves learning efficiency and accuracy, especially in data-scarce scenarios. We evaluate KDM using open-source Wi-Fi and ADS-B datasets, and the results demonstrate that our method consistently outperforms existing state-of-the-art few-shot SEI approaches. For example, on the ADS-B dataset, KDM boosts accuracy from 60.99% to 75.34% as the sample count increases from 5-shot to 10-shot, surpassing other methods by over 10%. Similarly, on the Wi-Fi dataset, KDM achieves an impressive 88.94% accuracy in low-sample (5-shot) scenarios. The codes are available at <uri>https://github.com/tengmouren/KDM2SEI</uri>.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"3247-3259"},"PeriodicalIF":6.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599002","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

Joint Augmentation and Part Learning for Unsupervised Clothing Change Person Re-Identification

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/TIFS.2025.3550063

Zhiqi Pang;Lingling Zhao;Yang Liu;Gaurav Sharma;Chunyu Wang

{"title":"Joint Augmentation and Part Learning for Unsupervised Clothing Change Person Re-Identification","authors":"Zhiqi Pang;Lingling Zhao;Yang Liu;Gaurav Sharma;Chunyu Wang","doi":"10.1109/TIFS.2025.3550063","DOIUrl":"10.1109/TIFS.2025.3550063","url":null,"abstract":"Clothing change person re-identification (CC-ReID) is a crucial task in intelligent surveillance, aiming to match images of the same person wearing different clothing. Promising performance in existing CC-ReID methods is achieved at the cost of labor-intensive manual annotation of identity labels. While some researchers have explored unsupervised CC-ReID, these methods still depend on additional deep learning models for preprocessing. To eliminate the need for additional models and improve performance, we propose a joint augmentation and part learning (JAPL) framework that obtains clothing change positive pairs in an unsupervised fashion by synergistically combining augmentation-based invariant learning (AugIL) and part-based invariant learning (ParIL). AugIL first constructs clothing change pseudo-positive pairs and then encourages the model to focus on clothing-invariant information by enhancing feature consistency between the pseudo-positive pairs. ParIL beneficially encourages high similarity between inter-cluster clothing change positive pair using part images and a prediction sharpening loss. PartIL also introduces a soft consistency loss that promotes clothing-invariant feature learning by encouraging consistency of class vectors between the real features actually used for CC-ReID and the part features. Experimental results on multiple ReID datasets demonstrate that the proposed JAPL not only surpasses existing unsupervised methods but also achieves competitive performance compared to some supervised CC-ReID methods.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"20 ","pages":"2944-2956"},"PeriodicalIF":6.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598866","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

HTOTP: Honey Time-based One-Time Passwords

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-03-11 DOI: 10.1109/tifs.2025.3550075

Zixuan Ding, Ding Wang

引用次数: 0