In recent years, WiFi sensing has been recognized as a promising technology to bring respiratory monitoring into everyday homes, thanks to its contactless nature and ubiquitous availability. However, existing WiFi-based respiratory monitoring systems still fall short of deployment-oriented performance: they suffer from restrained hardware scalability, limited accuracy, and are highly sensitive to user location. To overcome these limitations and push WiFi sensing towards clinically meaningful precision, we propose RespirFi, a novel system that robustly delivers high-fidelity respiratory waveforms with WiFi Channel State Information (CSI), thereby enabling accurate estimation of key physiological biomarkers. At the core of RespirFi is a theoretical human reflection model, through which we perform an in-depth characterization of how CSI variations are shaped by both subcarrier frequency and spatial user location. Guided by these insights, we develop a location-robust waveform construction method that adaptively selects high quality subcarriers and aligns their waveform trends, ensuring accurate waveform recovery. Furthermore, we propose a breathing phase identification method that leverages inter-subcarrier CSI differences to reliably distinguish inhalation from exhalation. We implement RespirFi over commodity WiFi devices, and extensive experiments demonstrate that it outperforms state-of-the-art approaches across a wide range of clinically relevant respiratory metrics.

Issuer-Hiding Anonymous Credential (IHAC) is a cryptographic primitive, which enables users to authenticate themselves with fine-grained control over their credentials (certified attributes), without revealing the credential issuers. A state-of-the-art IHAC scheme, designed for Self-Sovereign Identity systems, was recently published by Sanders and Traoré at PET’24. However, this scheme does not support the revocation of credentials. It is well-known that revocation is essential for many secure applications. In this work, we introduce a revocable IHAC scheme. Our contributions are three-fold. First, we use the classical pairing-based accumulator with Sanders and Traoré’s scheme to provide user revocation without compromising its unique features like compactness and issuer-hiding. Second, we prove the security of our construction under the well-studied q-Strong Diffie-Hellman (q-SDH) assumption, rather than the Generic Group Model (GGM) originally used by Sanders and Traoré. Third, our construction ensures optimal efficiency, as the accumulator-based revocation incurs only a small extra overhead to PET’24. Our implementation shows that when using 100 certified attributes and 20 issuers in an anonymous credential system, the and protocol takes 3.15 s in total, only incurring a 2.5 % increase compared to Sanders and Traoré’s scheme.

The emergent direct-to-cell Low-Earth Orbit (LEO) satellite mega-constellations promise ubiquitous LTE/5G access for mobile users without terrestrial base stations. However, it still faces fake satellite attacks during the bootstrapping process, stemming from the openness of satellite-terrestrial links and the substantially higher user access demands imposed by global coverage compared to terrestrial base stations. Existing protocols make optimistic assumptions regarding satellite trustworthiness. Moreover, their poor scalability results in performance bottlenecks when handling massive authentication requests within short timeframes. To address these challenges, we propose a lightweight and secure extended authentication and key agreement (eAKA) protocol under the Dolev Yao model. The protocol integrates a certificateless signature scheme to resist fake satellite attacks, a lightweight AMAD-based batch authentication mechanism to improve efficiency, and a group key update mechanism based on the Chinese Remainder Theorem (CRT) to support dynamic user management and enhance scalability. Rigorous analysis demonstrates the protocol satisfies satellite authentication security requirements. Compared with the benchmark, it reduces computational, communication, and transmission overhead by $17.24 \%, 36.77 \%$, and 50 %, respectively.

Unmanned aerial vehicles (UAVs) can be utilized effectively as airborne base stations, offering wireless communication and federated learning (FL) services for terrestrial edge devices (EDs). FL enables EDs to collaboratively train a global model for specific tasks without sharing their local raw data. However, due to the unreliable wireless links and dynamic topologies, potential malicious nodes may attempt to masquerade as legitimate nodes to pose various potential threats (e.g., inference, poisoning, and backdoor attacks) to undermine the trustworthiness of the intermediate model parameters. Although existing cryptographic authentication protocols focus on the data-level security, security level can be further enhanced at the physical layer level. In this paper, we design FedPLA, a UAV-aided federated learning framework enhanced by physical layer authentication, which utilizes SNR difference for lightweight authentication to ensure FL intrinsic security. Subsequently, we formulate a multi-step decision problem for joint UAV trajectory and resource allocation, aiming to minimize the latency and energy costs while maximizing secure edge device number with intrusion-proof and unjustly accused-proof guaranty. To efficiently deduce strategies while avoiding potential dangerous policies, we develop an LSTM-enhanced safe deep reinforcement learning algorithm (LSTM-SDRL) for real-time strategy making. Furthermore, extensive simulations demonstrate the effectiveness of the proposed LSTM-SDRL.

As the cornerstone of telemedicine, Electronic Health Records (EHRs) not only reduce clinical costs but also enable precision diagnostics. As medical institutions increasingly outsource EHRs to Cloud Service Providers (CSPs), dual challenges have emerged as critical issues: preserving patient privacy and enhancing the search experience for medical personnel. While multi-keyword searchable encryption has gained significant attention in the medical domain as a potential solution, existing schemes have significant limitations in both practicality and security. First, the growing number of medical institutions complicates the management of key and privileges. Second, the impoverished search semantics in existing query mechanisms severely degrades the clinical user experience, creating unacceptable operational bottlenecks in medical practice. Furthermore, excessive reliance on CSPs leads to ignoring situations where the returned results are incorrect, impacting the availability and security of the telemedicine system. To address these limitations, we propose a Verifiable Multi-Semantic Keyword Search scheme (VMSKS) for EHRs in cloud-based telemedicine. To resolve the security requirements arising from the increasing medical institutions, we innovatively design a more efficient dual Secure k-Nearest Neighbor technique (SKNN) for key distribution. Meanwhile, fine-grained access control is implemented using access policy trees, ensuring the controllability of data access. This approach safeguards the privacy of EHRs. To support flexible EHR search for medical personnel, the prime Hadamard product encoding technique is exploited to provide queries that support multiple search semantics simultaneously. Given the potential unreliability of CSPs, VMSKS introduces a novel verification mechanism by constructing verification proofs during encryption, ensuring the authenticity and integrity of returned results. Theoretical analysis and experimental evaluation demonstrate the security and efficiency of VMSKS, respectively.

The increasing complexity of the smart grid (SG) raises significant concerns regarding the security of SG communication. As a countermeasure, the authentication and key agreement (AKA) protocol ensures the secure transmission of sensitive information between legitimate entities by achieving mutual authentication and establishing session keys. One of the most urgent and critical security threats in the AKA protocol concerns the ephemeral secret leakage (ESL) attack, due to its threat to session key secrecy. However, there remains a lack of systematic understanding of how to resist ESL attacks in the SG environment. Therefore, we categorize the ESL attack into three different types, then conduct an in-depth analysis of their root causes and propose corresponding recommendations to mitigate it. To further illustrate the effectiveness of the recommendations, we design a secure and efficient AKA protocol based on elliptic curve cryptography accordingly. The proposed protocol is proven secure through rigorous security proof under the random oracle model and formally verified by the AVISPA tool. Performance comparisons indicate that the proposed protocol outperforms other related protocols due to its lightweight nature and adherence to all fundamental security attributes, making it well-suited for deployment in a resource-constrained SG environment.

Federated learning (FL) has emerged as a promising technique in the Industrial Internet of Things (IIoT) by enabling distributed devices to collaboratively train models without sharing raw data. In FL, ensuring data privacy and secure authentication becomes essential due to the sensitivity of industrial data and the potential for adversarial attacks. This article highlights a security flaw in a recently proposed FL authentication protocol designed for IIoT environments. Specifically, the scheme is analyzed to be susceptible to public-key replacement attacks. We propose a secure lightweight authentication scheme for privacy-enhanced (SLAPE) FL to address vulnerabilities in participant registration, group key distribution, local data training, and aggregation processes. SLAPE leverages the elliptic curve cryptography (ECC) with the Chinese remainder theorem (CRT) to support malicious group member traceability, revocation of compromised identities, and efficient batch verification of multiple messages. It effectively resists Type-I attacks that previous schemes could not, while also incorporating forward and backward security essential for IIoT applications. We rigorously demonstrate SLAPE's resilience against prevalent threats through both formal and informal analyses. Our evaluation results indicate that SLAPE demonstrably enhances the security and privacy of existing schemes, with improvements in computational efficiency for both proof generation and verification, while keeping communication overhead relatively low.

With the development of air transportation, Space-Air-Ground Integrated Network (SAGIN) are playing an increasingly important role in optimizing air traffic management and enhancing flight safety for billions of passengers and trillions dollars of aviation industry. As the key technology of SAGIN, the Automatic Dependent Surveillance-Broadcast (ADS-B) system is widely used due to its simple operation, low construction cost, and high information accuracy. However, the security problems in ADS-B system, including lack of identity authentication between all communication links, crucial information transmitted in plaintext, and susceptibility to the single point of failure, have been serious obstacle to its wide application. Existing solutions fail to account for the unique characteristics of ADS-B and SAGIN, leading to inadequate security and poor performance in these specialized contexts. Aiming to solve the above issues and provide security and scalability for ADS-B system, we conduct the following research. Firstly, an enhanced identity-based broadcast signcryption (e-IBBSC) scheme is designed to keep crucial information confidential and all messages authenticated simultaneously. Secondly, we propose an efficient batch message authentication method combined with the Merkle tree and proposed e-IBBSC, significantly improving the ADS-B message utilization ratio from 1.35% to 74.10%. Thirdly, we utilize the sharding blockchain and Byzantine fault tolerance protocol to design the first sharding-based distributed management system for SAGIN that realizes fault tolerance and scalability. Finally, after a detailed security analysis and comprehensive performance evaluation, we demonstrate that our solution can achieve all proposed system goals including security, scalability, and high performance of 1s flight transaction processing latency and 62KTPS throughput.

Fully homomorphic encryption (FHE) is a type of cryptographic primitives known for its high-computation but low-communication costs in carrying out secure multi-party computation. However, due to the large ciphertext expansions and complex computing paradigm, designs and implementations of foundational FHE infrastructures still have much to be desired. In this work, we propose HALO, the first homomorphic instruction set architecture that supports efficient evaluations of both arithmetic and logic circuits over FHE ciphertexts. We construct a new layer of abstraction for FHE by identifying unique data structures, cryptographic primitives and homomorphic operators. We provide an open-source implementation for all instructions and data types in HALO, and demonstrate that our implementation can be 1.7x-11x faster than similar implementations across test benchmarks. Moreover, we show that HALO can be 1.7x-5.4x faster than the prior work over a set of end-to-end neural network benchmarks.

IoT devices have become deeply integrated into our daily lives, making comprehensive security research on critical infrastructure devices increasingly important. Static analysis techniques, particularly those leveraging taint propagation, have demonstrated promise in identifying security vulnerabilities within these devices, effectively detecting critical vulnerabilities. However, current solutions often struggle with limitations in both detection efficiency and accuracy. To address these challenges, this paper introduces Liva, a novel static taint analysis tool designed for detecting web vulnerabilities in IoT devices. Liva employs a large language model (LLM) multi-agent approach for static binary taint analysis, primarily leveraging fine-tuned open-source models and commercial LLMs to improve source/sink identification and taint data analysis-areas where traditional methods often fall short-thereby enhancing overall analysis efficiency. LIVA's core analysis engine leverages a Qwen3-32B open-source model that has been fine-tuned using a dataset of 3,000 real-world device samples. This fine-tuned model achieves a 3 percentage point improvement in accuracy for identifying taint data propagation relationships compared to commercial LLMs, while also increasing average analysis efficiency by 5.5%. A comprehensive evaluation of Liva, conducted on a dataset of 64 devices from 11 vendors, revealed that it detected 309 and 349 more known vulnerabilities than the state-of-the-art solutions SaTC and Karonte, respectively, while simultaneously reducing false positive rates by 59.4% and 67.6%. Liva achieves a recall of 98.1% and a precision of 74.6%, with a 6.7 & times; reduction in analysis time compared to the best-performing baseline. Furthermore, in the realm of zero-day vulnerability detection, Liva discovered 64 previously unknown vulnerabilities, 39 of which have since been assigned official CVE/CNVD identifiers.

Large Language Models (LLMs) sparked massive applications in 6G. However, the emerging 6G Mobile Edge Computing (MEC) based on LLM caching leaves model protection unconsidered. To protect the LLM assets under the extended Dolev-Yao (DY) threat model, a secure batch LLMs deployment framework is proposed for 6G MEC, which securely delivers the sanitized crafted (san-crafted) LLM and the crafted-random-values (CR-values) from 6G edge to the Rich Execution Environment (REE) and Trusted Execution Environment (TEE) of mobile devices, respectively. Firstly, the 6G MEC cached LLM is san-crafted by an efficiency-improved sanitizable signature to protect the integrity of the LLM during the entire deployment process. Then, a lightweight batch authentication protocol is proposed to improve the efficiency of verifying ultra-massive model requests. Finally, to be compatible with the state-of-the-art secure inference (i.e., Magnitude), the san-crafted LLM delivered into mobile device's REE is verified by sanitizable signatures, and then conducts secure inference with the corresponding CR-values provisioned into the TEE. Rigorous security proofs confirm that our framework meets the security requirements of LLM deployment. Compared to the benchmark, the proposed framework significantly improves both computational and communication efficiency, reducing computational overhead by 89.92% and communication overhead by 47.08%. This framework facilitates the TEE-based LLMs secure inference for ultra-massive mobile devices in 6G MEC.

In the above paper, Shariq et al. proposed PSRSD2D, a drone-to-drone (D2D) authentication scheme claimed to provide multiple security properties. However, our analysis indicates that an adversary can launch drone capture attacks against two drones to recover the root secrets of the trusted authority, leading to a system-wide breakdown. Moreover, PSRSD2D is vulnerable to ephemeral secret leakage attacks and fails to achieve forward secrecy. After analyzing the underlying causes of these flaws, we introduce targeted modifications. The enhanced protocol mitigates the vulnerabilities in PSRS-D2D and provides stronger security guarantees.

With the continuous expansion of Web service functionalities, the security threats they face have become increasingly severe, leading to growing demands for service security and reliability. As service resources scale up, the attack surface widens, and attacker capabilities strengthen, resulting in a rise in unknown threats and swarm attack. Particularly in real-world network environments where large amounts of unlabeled traffic data exist, key challenges include achieving lightweight characterization of Web service traffic interactions, effectively detecting multi-source threats, and constructing a detection model with good interpretability in an unlabeled environment . These issues are critical to overcoming current security bottlenecks. To address these challenges, a lightweight traffic interaction graph construction method based on traffic analysis for efficient characterization was proposed. By introducing the structural entropy game partitioning mechanism, the adaptive partitioning of the communities in the traffic interaction graph has been achieved. On this basis, the topological characteristics of communities subgraphs were analyzed, and key communities were identified through graph metric ranking. Furthermore, a Z-score-based adaptive threat vertex detection method was proposed to effectively identify high-threat vertices in key communities. Simultaneously, a structural entropy-based swarm threat detection method was introduced to accurately detect swarm threat entities. The proposed method operates without relying on prior knowledge or data labels, while maintaining adaptability to unknown threats, robustness to encrypted traffic, and good interpretability. Experimental results on public datasets and real-world network traffic data demonstrated that the method could effectively identify various entities and attack behaviors that threaten the availability of Web services, achieving high detection accuracy and practical engineering application value.

With the development of quantum computing technology, the threat of quantum computing to classical cryptography has been significantly enhanced. Studying the impact of quantum computing on the security of classical cryptosystems is a research focus in the cryptographic community. Pseudorandom functions are fundamental building blocks in numerous cryptosystems and play a critical role in post-quantum cryptography. To the best of our knowledge, research on effective quantum attacks on pseudorandom functions remains limited in the noisy intermediate-scale quantum era. This paper proposes a distributed quantum key recovery attack on pseudorandom functions and designs the corresponding quantum circuit based on distributed quantum computing technology, Bernstein-Vazirani’s and Grover’s algorithms. The proposed attack is applied to two pseudorandom functions, pEDM and SoKAC. Compared with the existing attacks, the proposed attack has lower complexity and stronger robustness to circuit noise. The proposed attack can realize effective quantum key recovery and provide theoretical support for designing quantum secure pseudorandom functions.

To promote the practicality of deep learning-based localization, existing studies aim to address the issue of scenario dependence through meta-learning. However, these studies primarily focus on variations in environmental layouts while overlooking the impact of changes in device configurations, such as bandwidth, the number of access points (APs), and the number of antennas used. Unlike environmental changes, variations in device configurations affect the dimensionality of channel state information (CSI), thereby compromising neural network usability. To address this issue, we propose Meta-SimGNN, a novel WiFi localization system that integrates graph neural networks with meta-learning to improve localization generalization and robustness. First, we introduce a fine-grained CSI graph construction scheme, where each AP is treated as a graph node, allowing for adaptability to changes in the number of APs. To structure the features of each node, we propose an amplitude-phase fusion method and a feature extraction method. The former utilizes both amplitude and phase to construct CSI images, enhancing data reliability, while the latter extracts dimension-consistent features to address variations in bandwidth and the number of antennas. Second, a similarity-guided meta-learning strategy is developed to enhance adaptability in diverse scenarios. The initial model parameters for the fine-tuning stage are determined by comparing the similarity between the new scenario and historical scenarios, facilitating rapid adaptation of the model to the new localization scenario. Extensive experimental results over commodity WiFi devices in different scenarios show that Meta-SimGNN outperforms the baseline methods in terms of localization generalization and accuracy.

Smart grid (SG) facilitates our lives by providing more reliable electricity and enabling better integration of renewable energy sources. Currently, numerous authentication and key agreement (AKA) protocols have been proposed to secure SG communication. However, these solutions often result in considerable cost, making them inappropriate for resource-constrained SG environment. In this paper, we propose a secure and resource-efficient AKA protocol by employing lightweight cryptography primitives including authenticated encryption with associated data (AEAD) primitive ASCON, hash function and XOR operation. The ASCON primitive simultaneously provides data confidentiality, integrity and authenticity with low computation cost, making it suitable for employing in resource-constrained SG environment. The secret intermediate values in the protocol are designed as hash values that incorporate both long-term and short-term secrets, thereby providing enhanced security while further reducing cost. Moreover, a dynamic indexing method is deployed in the protocol to resist de-synchronization attack. The designed protocol performs secure mutual authentication and session key establishment between entities without relying on a central trusted authority. The proposed protocol is proven secure through rigorous security proof under the real-or-random model and formally verified by AVISPA tool. Theoretical performance analysis and simulation results indicate that the proposed protocol outperforms other related protocols due to its lightweight nature and adherence to all fundamental security attributes, making it suitable for deployment in smart grid environment.

Face authentication (FA) schemes are universally adopted. However, current FA systems are mainly camera-based and susceptible to masks and vulnerable to spoofing attacks. This paper exploits the penetrability, material sensitivity, and fine-grainedsensing capability of millimeter wave (mmWave) to build an anti-spoofing FA system, named mmFace. It scans faces by movinga commodity mmWave radar along a specific trajectory. The signals bounced off the face carry facial biometric and structure features, which allows mmFace to achieve reliable liveness detection and FA. Due to the penetrability of mmWave, mmFace can still work well when users wear masks. To en- hance security, we develop a liveness detection method and an amplitude modulation-based method to defend against spoofing attacks and replay attacks. We enhance the basic version of mmFace (Xu et al., 2022) by improving its performance under mask occlusion and replay attack resilience. Besides, we explore a distance-resistant structure feature to suppress the impact of unstable face- to-device distance. To avoid on-site registration, we propose a novel virtual registration approach based on the cross-modal transformation from photos to mmWave. We implement mmFace with various antenna configurations and prototype two typical modes of mmFace. Extensive experiments demonstrate mmFace's accuracy in FA and effectiveness in attack detection.

Dynamic-committee Proactive Secret Sharing (DPSS) has gained increased attention for its ability to dynamically update the shareholder committees and refresh secret shares, even against adversaries that gradually corrupt all nodes. However, existing state-of-the-art asynchronous DPSS protocols suffer from significant $\mathcal{O}\left(n^{3}\right)$ message complexity and $\mathcal{O}\left(\lambda n^{3}\right)$ communication complexity, where $\lambda$ denotes the security parameter and n is the committee size. In this paper, we distinguish optimistic-case and worstcase scenarios based on node behaviors and network conditions, thus reducing the redundant communication overhead of asynchronous DPSS. Under the trusted setup assumption, we achieved an $\mathcal{O}\left(n^{2}\right)$ message complexity in all scenarios. Additionally, our protocol has an $\mathcal{O}\left(\lambda n^{2}\right)$ communication complexity in the optimistic case, where all nodes are honest and the network is synchronous, and $\mathcal{O}\left(\lambda n^{3}\right)$ communication complexity in the worst case. We also propose two strategies to eliminate the strong trusted setup assumptions, and the asymptotic performance still surpasses the state-of-the-art protocols. For committee sizes of 4 to 400, the estimated concrete communication cost of our DPSS is 19-100x (resp., 8-14x) smaller in the optimistic case (resp., worst case) compared to LongLive (USENIX Security '23). Experiments in AWS show that our DPSS achieves a latency of $\mathbf{1. 9 - 8}$ seconds for committee sizes from 4 to 64. Single-machine benchmarks reveal a (computational) runtime reduction of up to 44 %.

Abstract Unmanned aerial vehicles (UAVs) have been increasingly integrated into diverse domains such as environmental monitoring, intelligent transportation, border surveillance, and military reconnaissance, giving rise to the broader concept of the Internet of Drones (IoD). However, this rapid proliferation also underscores the urgent need for secure and efficient communication mechanisms, as UAVs typically operate over public channels that are highly vulnerable to various security and privacy threats. To address these issues, authentication and key agreement (AKA) protocols have been introduced to enhance secure communication. However, most of the existing AKA protocols either incur high computation cost due to complex cryptographic primitives, or fail to provide resilience against attacks such as replay, impersonation, and ephemeral secret leakage. In this paper, we propose an ultra-lightweight AKA protocol that integrates physical unclonable function (PUF) with the lightweight authenticated encryption with associated data (AEAD) primitive ASCON. The protocol supports UAV registration over open channels, achieves mutual authentication between UAV and ground station, and extends to secure UAV-UAV communication with the assistance of the ground station. A formal proof under the real-or-random (ROR) model demonstrates the semantic security of the proposed protocol, while informal analysis confirms robustness against diverse attacks. Comprehensive performance evaluation shows that the total computation cost of the proposed protocol is approximately 2.391 ms, which is the lowest among the compared schemes. Specifically, it reduces computation cost by up to 69.0% compared with representative IoD authentication protocols and remains approximately 10.9% lower than existing ultra-lightweight designs. These results demonstrate that the proposed protocol achieves a superior balance between security strength and resource efficiency, making it particularly suitable for resource-constrained IoD environments.

Recently, Karmakar et al. designed a mutual authentication and key agreement mechanism for an unmanned aerial vehicle (UAV) and the ground station. They claimed that their mechanism is resilient against common security threats. However, our analysis reveals that their mechanism fails to resist eavesdropping attack and ephemeral secret leakage attack. Moreover, this mechanism further becomes vulnerable to the impersonation attack when the ephemeral secret is leaked. Specifically, an adversary can obtain the session key established in the current session in the event that an ephemeral secret is leaked. After identifying the root causes of these vulnerabilities, we propose targeted countermeasures to strengthen protocol security.