In the field of multi-modal remote sensing semantic segmentation, synergistically utilizing optical and Synthetic Aperture Radar (SAR) images can significantly improve interpretation accuracy in complex scenes. However, existing methods face challenges when processing high-resolution heterogeneous data, including excessively high computational complexity, difficulties in maintaining the geometric consistency of long-span continuous ground objects (such as road networks), and obstacles in aligning modal features. This paper proposes Sigma-Fusion, a novel multi-modal segmentation framework based on the Mamba architecture. Firstly, the framework automatically suppresses speckle noise using a lightweight SAR Adapter. Subsequently, it constructs a Siamese Visual State Space Model (VSSM) that extracts long-range contextual dependencies for optical and SAR images separately. By capturing spatial structural features across the entire image at a linear computational cost, this model fundamentally alleviates the fragmentation of elongated objects -a problem inherent to traditional local convolutions. Finally, a multi-level Cross-Mamba Fusion mechanism is utilized to achieve bidirectional feature routing and deep calibration between modalities at multiple scales. Experiments on the WHU-OPT-SAR dataset demonstrate that Sigma-Fusion improves the standard mIoU and OA metrics by 2.72% and 1.00% compared to mainstream methods. To rigorously verify the core advantage of our model in geometric integrity, we introduce Boundary IoU as a key evaluation metric. Experimental results show that Sigma-Fusion achieves significant gains in boundary fidelity, fully validating its excellent efficacy with the help of Mamba’s global receptive field in high-resolution scenarios.

Federated Learning (FL) preserves client data privacy by distributing model training but remains vulnerable to inference attacks (e.g., gradient inversion). Existing secure aggregation schemes mitigate basic privacy threats, but most of them are under the semi-honest server assumption. Malicious servers can corrupt the global model through forging aggregation results. Moreover, the high interaction rounds and communication complexity of the existing schemes still constrain their feasibility in large-scale distributed deployment scenarios. To tackle these challenges, we propose EvaFL, an efficient verifiable privacy-preserving federated learning against malicious servers, which reduces the communication overhead and privacy threats from malicious severs. We propose the system model of EvaFL and give the concrete protocol. We leverage the linear homomorphism property of Shamir secret sharing under discrete logarithm assumption to reuse the mask seed shares, which avoids the communication overhead caused by share distribution in multiple rounds of iterations. In addition, by integrating consistency checking into the unmasking step, we further reduce one round interaction. To resist malicious servers, we adopt linear homomorphic hash to realize the correctness verification of the aggregation results. Finally, we implement and evaluate our EvaFL based on MNIST and CIFAR10 datasets to show its feasibility for privacy training. The single round aggregation completion time of EvaFL is reduced by 69% compared to BBGLR (CCS 2020) and by 11% compared to Flamingo (S&P 2023).

Centralized issuer in Anonymous Counting Tokens (ACT) is prone to single-point failure and imposes prohibitive computational overhead on resource-constrained IoT devices, hindering practical deployment. To overcome these limitations, we propose a user-side pairing-free lightweight distributed anonymous counting tokens protocol called LDACT. LDACT enables efficient issuance within a distributed environment and ensures that each client receives at most one valid token per message without disclosing their identity. LDACT eliminates pairing operations for user-side, enhancing scalability for source-constrained scenarios. Additionally, the tokens are publicly verifiable, allowing any party to verify their validity without compromising user anonymity. We conduct security analysis that LDACT satisfies unforgeability and unlinkability. We evaluate the computational overhead of LDACT on both Ubuntu and Raspberry Pi system, and compare it with other schemes. The result of the experiment demonstrates that LDACT achieves computational overhead in milliseconds for source-constrained IoT devices.

In NDSS 2024, Yu et al. proposed AAKA, an Anonymous Authentication and Key Agreement scheme designed to protect users' privacy from mobile tracking by Mobile Network Operators (MNOs). AAKA aims to provide both anti-tracking privacy and traceability (lawful de-anonymization), allowing subscribers to access the network via anonymous proofs while enabling a Law Enforcement Agency (LEA) to trace the real identity if misbehaviors are detected. However, we identify that the AAKA scheme in NDSS 2024 is insecure since the subscriber's identity is exposed within the protocol, thereby failing to achieve the claimed privacy and traceability. Building on the repair of AAKA, we propose AAKA +, Anonymous Authentication and Key Agreement with Verifier-Local Revocation, a new mobile authentication scheme, to ensure privacy against mobile tracking. In addition to the privacy and traceability introduced in NDSS 2024, AAKA + additionally allows the MNO to immediately assert whether the associated subscriber has been traced and revoked upon receiving an anonymous proof We formally define the syntax and the security model of AAKA + and propose two concrete schemes, AAKA+BB andAAKA+PS, based on the Boneh-Boyen signature and the Pointcheval-Sanders signature schemes, respectively. Both AAKA+BB and AAKA+PS are pairing-free on the user equipment side and compatible with existing cellular infrastructure. Experimental results show that our schemes are practical, with anonymous proof generation taking approximately 18 milliseconds for a constrained device.

Radar-based human activity recognition (HAR) has gained significant attention due to its advantages in privacy protection, interference resistance, and low power consumption. However, existing systems face challenges such as slow model processing speed, limited practical applicability, and a strong dependence on large labeled datasets. A new radar-based HAR system designed for multi-scenario and multi-directional application is proposed in this paper. First, a small-sample human activity dataset (MSMD-RHD) is developed, capturing activities from multiple directions and scenarios to better simulate real-life conditions. Next, we propose a lightweight, multi-dimensional feature enhancement and fusion network, HogEffNet, which integrates EfficientNetB1 and Histogram of Oriented Gradients (HOG) to extract behavioral information from Time-Doppler (TD) maps. To mitigate overfitting caused by limited samples, we apply a staged transfer learning strategy to fine-tune the top parameters of the pre-trained EfficientNetB1. HOG is incorporated to address the insufficient ability of EfficientNetB1 in extracting low-dimensional local shape features, thereby improving robustness in multi-directional HAR. Furthermore, Sparse Autoencoder (SAE) is employed to fuse high-dimensional and low-dimensional features effectively. The proposed system is evaluated on two datasets. Ablation experiments conducted on the MSMD-RHD dataset achieve an accuracy of 98.8%, demonstrating the effectiveness of each component. Comparative experiments on the public dataset of University of Glasgow, UK, achieve an accuracy of 97.2%, surpassing recent state-of-the-art methods.

As online application services evolve, an increasing number of users are opting for subscription-based or paywall models to access high-quality content. Anonymous counting tokens (ACTs), which regulate user access while protecting user privacy, are widely adopted in the online paywall model. However, the centralized server of ACT may lead to a single point of failure, thereby exposing users’ privacy. To address this challenge, in this paper, we propose threshold anonymous counting tokens with batch proofs (ThrACT) that balance privacy preservation and access count limitation for online paywalls. We define the system model for ThrACT and provide its concrete construction. We utilize the threshold Boneh-Boyen signature to facilitate distributed issuance of anonymous tokens and enable batch issuance. In addition, our ThrACT employs non-interactive zero-knowledge proofs to verify the label and token requests while allowing the correctness of multiple blind token shares to be validated simultaneously. We also prove that ThrACT satisfies unforgeable and unlinkable security properties. Finally, we evaluate the computational cost of our ThrACT and compare it with other schemes. The experiment result demonstrates that ThrACT not only supports distributed issuance, batch verification, and counting functionalities but also achieves computational overhead in milliseconds. In particular, when the threshold is set to (3,5), the token issuance time is approximately 9 milliseconds.

The redactable blockchain provides the editability of blocks, which guarantees the data immutability of blocks while removing illegal content on the blockchain. However, the existing redactable blockchain relies on trusted assumptions regarding a single editing authority. Ateniese et al. (EuroS&P 2017) and Li et al. (TIFS 2023) proposed solutions by using threshold chameleon hash functions, but these lack accountability for malicious editing. This paper delves into this problem and proposes an accountability weight threshold blockchain editing scheme. Specifically, we first formalize the model of a redactable blockchain with accountability. Then, we introduce the novel concept of the Accountable Weight Threshold Chameleon Hash Function (AWTCH). This function collaboratively generates a chameleon hash trapdoor through a weight committee protocol, where only sets of committees meeting the weight threshold can edit data. Additionally, it incorporates a tracer to identify and hold accountable any disputing editors, thus enabling supervision of editing rights. We propose a generic construction for AWTCH. Then, we introduce an efficient construction of AWTCH and develop a redactable blockchain scheme by leveraging AWTCH. Finally, we demonstrate our scheme’s practicality. The editing efficiency of our scheme is twice that of Tian et al. (TIFS 2023) with the same number of editing blocks.

With the emergence of illegal behaviors such as money laundering and extortion, the regulation of privacy-preserving cryptocurrency has become increasingly important. However, existing regulated privacy-preserving cryptocurrencies usually rely on a single regulator, which seriously threatens users’ privacy once the regulator is corrupt. To address this issue, we propose a linkable group signature against malicious regulators (ALGS) for regulated privacy-preserving cryptocurrencies. Specifically, a set of regulators work together to regulate users’ behavior during cryptocurrencies transactions. Even if a certain number of regulators are corrupted, our scheme still ensures the identity security of a legal user. Meanwhile, our scheme can prevent double-spending during cryptocurrency transactions. We first propose the model of ALGS and define its security properties. Then, we present a concrete construction of ALGS, which provides CCA-2 anonymity, traceability, non-frameability, and linkability. We finally evaluate our ALGS scheme and report its advantages by comparing other schemes. The implementation result shows that the runtime of our signature algorithm is reduced by 17% compared to Emura et al. (2017) and 49% compared to KSS19 (Krenn et al. 2019), while the verification time is reduced by 31% compared to Emura et al. and 47% compared to KSS19.

Monero uses ring signatures to protect users’ privacy. However, Monero’s anonymity covers various illicit activities, such as money laundering, as it becomes difficult to identify and punish malicious users. Therefore, it is necessary to regulate illegal transactions while protecting the privacy of legal users. We present a revocable linkable ring signature scheme (RLRS), which balances the privacy and supervision for privacy-preserving blockchain transactions. By setting the role of revocation authority, we can trace the malicious user and revoke it in time. We define the security model of the revocable linkable ring signature and give the concrete construction of RLRS. We employ accumulator and ElGamal encryption to achieve the functionalities of revocation and tracing. In addition, we compress the ring signature size to the logarithmic level by using non-interactive sum arguments of knowledge (NISA). Then, we prove the security of RLRS, which satisfies anonymity, unforgeability, linkability, and non-frameability. Lastly, we compare RLRS with other ring signature schemes. RLRS is linkable, traceable, and revocable with logarithmic communication complexity and less computational overhead. We also implement RLRS scheme and the results show that its verification time is 1.5s with 500 ring members.

Smart healthcare leverages Internet of Things (IoT), wireless communication and cloud computing technologies in the medical industry to enable healthcare professionals and patients to deliver remote medical services, conduct intelligent monitoring and analyze diseases without being constrained by time and location. Wireless Body Area Network (WBAN), extensively utilized in this sector, is a wireless network composed of wearable or embedded devices placed in different parts of the human body to monitor and record human health signals continuously. However, a contradiction exists between identity authentication and privacy protection in WBANs, which necessitates addressing the challenge of balancing anonymity and traceability. The open wireless environment makes WBANs vulnerable to various attacks and security threats, while sensor nodes in these networks face limitations such as restricted computing power. This paper addresses practical concerns in WBANs including identity authentication and dynamic user management, and develops an effective privacy-preserving authentication scheme that incorporates revocability and conditional privacy protection. A novel revocable certificateless short signature algorithm is designed that not only has high execution efficiency but also utilizes the binary tree structure to achieve keys update and user revocation, ensuring high execution efficiency while addressing the issue of high key management complexity in traditional schemes. This scheme uses pseudonyms instead of real identity information in authentication request messages for anonymity, and tracks malicious users based on the (t, k) secret sharing mechanism. The papers security analysis shows that the scheme is unforgeable under the random oracle model (ROM) and can resist typical security threats meeting various security requirements. Compared with other related schemes, this scheme has higher communication and computational efficiency and is more suitable for WBAN environments.

Accurate medical image segmentation is crucial for precise diagnosis and treatment in clinical pathology analysis and surgical navigation. While Convolutional Neural Network (CNN)-based approaches excel in capturing and analyzing local features, they often lose key global context. Transformers, utilizing self-attention mechanisms, address this issue but often overlook localized and multi-scale features while also requiring significant computational resources. To integrate the advantages of CNNs and Transformers to achieve efficient and precise medical image segmentation, we propose a segmentation framework based on multi-feature fusion CNN and Bi-level Routing Attention Transformer (MCBTNet). MCBTNet integrates CNNs and Transformers within a U-shaped encoder-decoder architecture. This configuration not only extracts multi-scale features via the U-shaped structure but also efficiently captures global contextual information through the dynamic sparsity of the Bi-Level Routing Attention Transformer. Our novel Frequency-Channel-Spatial multi-dimensional attention mechanism is implemented on skip connections, enhancing segmentation accuracy and speed by maximizing multi-scale feature utilization. Finally, MCBTNet obtains the segmentation result by fusing the predictions of different scales. Experimental results on five public datasets demonstrate that MCBTNet outperforms state-of-the-art methods in Dice and HD metrics, with lower computational and memory requirements.

Identity-based threshold signature (IDTHS) allows a threshold number of signers to generate signatures to improve the deterministic wallet in the blockchain . However, the IDTHS scheme cannot determine the identity of malicious signers in case of misinformation . To solve this challenge, we propose an identity-based threshold (multi) signature with private accountability (for short AIDTHS) for privacy-preserving blockchain . From the public perspective, AIDTHS is completely private and no user knows who participated in generating the signature. At the same time, when there is a problem with the transaction, a trace entity can trace and be accountable to the signers. We formally define the syntax and security model of AIDTHS. To address the issue of identifying malicious signers, we improve upon traditional identity-based threshold signatures by incorporating zero-knowledge proofs as part of the signature and leveraging a tracer holding tracing keys to identify all signers. Additionally, to protect the privacy of signers, the signature is no longer achievable by anyone, which requires a combiner holding the keys to produce a valid signature. We give a concrete construction of AIDTHS and prove its security. Finally, we implement the AIDTHS scheme and compare it with existing schemes. The key distribution algorithm of AIDTHS takes 13.04 ms and the signature algorithm takes 34.60 μ s . The verification algorithm takes 1 s , which is one-third of the time the TAPS scheme uses.

Internet of Things (IoT) connects massive physical devices to capture and collect useful data, which are used to make accurate decisions by taking advantage of the machine learning techniques. However, the collected data may contain users’ sensitive information. When guaranteeing the utility of data, we need to consider privacy of users’ data. To balance the utility and the privacy of data, the existing approaches usually adopt the privacy-preserving signature technology, where the privacy-preserving data are classified by a designated converter (data processor) interacting with a semihonest verifier (data center). However, for the malicious behavior of the data center and data processor, this kind of approach is insufficient. To prevent the malicious data center/data processor while guaranteeing the utility and privacy of data, we propose blockchain-based auditable privacy-preserving data classification (PPDC) scheme for IoT. We put forth a new controllably linkable group signature (CL-GS) to balance the utility and privacy of data and take advantage of blockchain to audit the correctness of privacy-preserving data classification against malicious data processor/data center. We formalize the system model of the auditable privacy-preserving data classification in the blockchain setting and its security model. Then, we present a concrete construction and prove its security in the random oracle model. Finally, we deploy a prototype system to evaluate the performance ofPPDC.

Personal privacy disclosure is one of the most serious challenges in smart industrial applications. Anonymous authentication is an effective solution to protect personal privacy. However, the existing anonymous credential protocols are not perfectly suitablefor smart industrial environments such as smart vehicles in the sense that the credential revocation issue is not well-solved. In this article, we propose a Blockchain-based Anonymous authentication with Selective revocation for Smart industrial applications (BASS) for smart industrial applications supporting attribute privacy, selective revocation, credential soundness, and multishowing-unlinkability. Specifically, an efficient selective revocation mechanism is proposed based on dynamic accumulators and the signature algorithm due to Pointcheval and Sanders as the overlay of the BASS. According to the diverse demands of credential authorities, BASS can selectively provide revocation of credentials or revocation of users. We extend BASS from single-attribute privacy to multiattribute privacy as well. Finally, we implement a prototype to evaluate the cryptographic core primitives of BASS by deploying smart contracts in Ethereum to demonstrate the validity of BASS in smart industrial applications.

Currently, the number of Internet of Things (IoT) devices making up the IoT is more than 11 billion and this number has been continuously increasing. The prevalence of these devices leads to an emerging IoT business model called Device-as-a-service, which enables sensor devices to collect data disseminated to all interested devices. The devices sharing data with other devices could receive some financial reward, such as Bitcoin. However, side-channel attacks, which aim to exploit some information leaked from the IoT devices during data trade execution, are possible since most of the IoT devices are vulnerable to be hacked or compromised. Thus, it is challenging to securely realize data trading in IoT environment due to the information leakage, such as leaking the private key for signing a Bitcoin transaction in Bitcoin system. In this paper, we propose LRCoin, a kind of leakage-resilient cryptocurrency based on bitcoin in which the signature algorithm used for authenticating bitcoin transactions is leakage-resilient. LRCoin is suitable for the scenarios where information leakage is inevitable, such as IoT applications. Our core contribution is proposing an efficient bilinear-based continual-leakage-resilient ECDSA signature. We prove the proposed signature algorithm is unforgeable against adaptively chosen messages attack in the generic bilinear group model under the continual leakage setting. Both the theoretical analysis and the implementation demonstrate the practicability of the proposed scheme.

Cloud computing provides a new, attractive paradigm for the effective sharing of storage and computing resources among global consumers. More and more enterprises have begun to enter the field of cloud computing and storing data in the cloud to facilitate the sharing data among users. However, in many cases, users may be concerned about data privacy, trust, and integrity. It is challenging to provide data sharing services without sacrificing these security requirements. In this paper, a data sharing scheme of reliable, secure, and privacy protection based on general access structure is introduced. The proposed scheme is not only effective and flexible, but also is capable of protecting privacy for the cloud owner, supporting data sharing under supervision, enabling accountability of users’ decryption keys, and identifying cheaters if some users behave dishonestly. Security analysis and efficiency analysis demonstrate that our proposed scheme has better performance in computational costs compared with most related works. The scheme is versatile to be used in various environments. For example, it is particularly suitable to be employed to protect personal health data and medical diagnostic data in information medical environment.