Federated learning has drawn widespread interest from researchers, yet the data heterogeneity across edge clients remains a key challenge, often degrading model performance. Existing methods enhance model compatibility with data heterogeneity by splitting models and knowledge distillation. However, they neglect the insufficient communication bandwidth and computing power on the client, failing to strike an effective balance between addressing data heterogeneity and accommodating limited client resources. To tackle this limitation, we propose a personalized federated learning method based on cosine sparsification parameter packing and dual-weighted aggregation (FedCSPACK), which effectively leverages the limited client resources and reduces the impact of data heterogeneity on model performance. In FedCSPACK, the client packages model parameters and selects the most contributing parameter packages for sharing based on cosine similarity, effectively reducing bandwidth requirements. The client then generates a mask matrix anchored to the shared parameter package to improve the alignment and aggregation efficiency of sparse updates on the server. Furthermore, directional and distribution distance weights are embedded in the mask to implement a weighted-guided aggregation mechanism, enhancing the robustness and generalization performance of the global model. Extensive experiments across four datasets using ten state-of-the-art methods demonstrate that FedCSPACK effectively improves communication and computational efficiency while maintaining high model accuracy.

Benefiting from the Internet of Vehicles (IoV), modern vehicles are no longer just transportation, but enablers of safe, intelligent, and efficient driving and mobility services. Vehicles increasingly demand environmental information and entertainment resources during operation. These resources are hosted in the cloud, and vehicle terminals query and access them via keywords. However, the ciphertext of resource keywords and trapdoor are transmitted in complex network environments, where simple encryption methods are highly vulnerable to keyword guessing attacks (KGA). Furthermore, single query methods and suboptimal resource sharing mechanisms are no longer viable. Thus, we propose an encrypted resource sharing scheme resisting keyword guessing attacks (ERSRKGA) for the IoV. The ERSRKGA ensures information security through advanced encryption and decryption technologies, combined with re-randomization during transmission. Based on attribute-based encryption, vehicles obtain legitimate attribute weights and participate in resource sharing via a confidential attribute authentication mechanism. The scheme's flexible and refined query functionality meets vehicles' diverse search requirements, including AND, NOT, and wildcard operators, and realizes fine-grained access control. Subsequently, the correctness of ERSRKGA and its resistance to KGA are verified. We compare ERSRKGA with other state-of-the-art schemes in terms of functionality, computational complexity, time overhead, and energy consumption. Experimental results demonstrate that ERSRKGA outperforms the compared schemes.

Due to the convenience of real time monitoring and feedback, eHealth system is gaining its popularity. With the wide adoption of electronic health records (EHRs), the security issues arise at the same time. Data integrity is one of the most fundamental security requirements and many techniques have been extensively studied to provide integrity guarantee, such as digital signature. Among various signature schemes, certificateless signature enjoys the advantages of there is neither complicated certificate management nor the key escrow problem. In this paper, we study the particular security threats in eHealth systems and analyze the limitations of traditional certificateless signature schemes if being directly applied to protect data integrity. We show that there is a gap between the traditional threat model and the security threats in practice. To improve the security, we define an enhanced notion for the “normal” type adversary in certificateless signature. Then, a concrete construction secure in the enhance model is presented, which can withstand more powerful but realistic attacks. In addition, we provide experimental simulations to analyze the efficiency and security of our proposed scheme. The results demonstrate its utility in eHealth systems and other similar scenarios.

In industrial control systems (ICSs), safeguarding system security is vital, and intrusion detection is an effective protective measure. In real-world ICS environments, however, the training data for intrusion-detection models are dominated by normal traffic, with very few abnormal samples. This scarcity prevents the model from fully learning abnormal characteristics, thereby limiting anomaly-detection performance. ICS traffic is also noisy and often captured incompletely, which is especially problematic because it possesses strong temporal dependencies. Moreover, ICS operations tend to be highly regular and follow a strict logical order, causing data to cluster densely in certain regions while edge regions remain sparsely populated—an imbalance that easily leads to misclassification. To tackle data imbalance, missing temporal features, and edge-sample scarcity, we propose the DualCGAN-AE approach. It employs two conditional generative adversarial networks to perform dynamic learning on global and edge features and to enrich samples with temporal information; a multimodel discriminator is incorporated to enhance training stability. We validated the method on two datasets, assessing both the quality of the generated samples and their impact on intrusion detection performance. Experimental results show that the samples produced by DualCGAN-AE significantly boost detection accuracy.

Unmanned Aerial Vehicle (UAV) swarms are a cornerstone technology in the rapidly growing low-altitude economy, with significant applications in logistics, smart cities, and emergency response. However, their deployment is constrained by challenges in secure communication, dynamic group coordination, and resource constraints. Although there are various cryptographic techniques, efficient and scalable group key management plays a critical role in secure task allocation in UAV swarms. Existing group key agreement schemes, both symmetric and asymmetric, often fail to adequately address these challenges due to their reliance on centralized control, high computational overhead, sender restrictions, and insufficient protection against physical attacks. To address these issues, we propose PCDCB (Pairing-free Certificateless Dynamic Contributory Broadcast encryption), a blockchain-assisted lightweight key management scheme designed for UAV swarm task allocation. PCDCB is particularly suitable for swarm operations as it supports efficient one-to-many broadcast of task commands, enables dynamic node join/leave, and eliminates key escrow by combining certificateless cryptography with Physical Unclonable Functions (PUFs) for hardware-bound key regeneration. Blockchain is used to maintain tamper-resistant update tables and ensure auditability, while a privacy-preserving mechanism with pseudonyms and a round mapping table provides task anonymity and unlinkability. Comprehensive security analysis confirms that PCDCB is secure and resistant to multiple attacks. Performance evaluation shows that, in large-scale swarm scenarios (n = 100), PCDCB reduces the cost of group key computation by 54.4% (up to 96.9%) and reduces the time to generate the decryption keys by at least 29.7%. In addition, PCDCB achieves the lowest communication cost among all compared schemes and demonstrates strong scalability with increasing group size.

With the substantial increase in the number of antennas, polar-domain channel modeling for extremely large-scale antenna array (ELAA) systems has been introduced to capture both angular and distance information in near-field environments. The fine-grained polar-domain channel provides additional sources of randomness, making it well-suited for physical layer key generation (PLKG). To minimize the pilot overhead in multi-user key generation and leverage the randomness from the polar-domain channel paths, we herein design a zero-forcing (ZF)-based precoding scheme to mitigate inter-path and inter-user interference. Using ZF precoding, we derive an analytical expression for the sum secret key rate (SKR) as a function of power allocation variables, and then optimize these variables in the presence of eavesdroppers. Our simulations validate the proposed precoding design and power allocation methods in terms of sum SKR versus the transmit power, antenna configurations, and spatial correlation between legitimate and eavesdropping channels.

Currently, deep learning models are easily exposed to data leakage risks. As a distributed model, Split Learning thus emerged as a solution to address this issue. The model is splitted to avoid data uploading to the server and reduce computing requirements while ensuring data privacy and security. However, the transmission of data between clients and server creates a potential vulnerability. In particular, model is vulnerable to intellectual property (IP) infringement such as piracy. Alarmingly, a dedicated copyright protection framework tailored for Split Learning models is still lacking. To this end, we propose the first copyright protection scheme for Split Learning model, leveraging fingerprint to ensure effective and robust copyright protection. The proposed method first generates a set of specifically designed adversarial examples. Then, we select those examples that would induce misclassifications to form the fingerprint set. These adversarial examples are embedded as fingerprints into the model during the training process. Exhaustive experiments highlight the effectiveness of the scheme. This is demonstrated by a remarkable fingerprint verification success rate (FVSR) of 100% on MNIST, 98% on CIFAR-10, and 100% on ImageNet, respectively. Meanwhile, the model’s accuracy only decreases slightly, indicating that the embedded fingerprints do not compromise model performance. Even under label inference attack, our approach consistently achieves a high fingerprint verification success rate that ensures robust verification.

This paper investigates the secret key capacity of key generation in the slow-fading wireless channel by considering the temporal correlation between channel measurements in the previous and current frame. We derive the analytical expression of secret key capacity in the slow-fading channel, and propose the design guideline for practical key generation schemes in slow fading channel environments. We systematically analyze the impact of all corresponding parameters on secret key capacity, including sampling period, sampling delay, eavesdroppers' location, signal-to-noise ratio, Doppler spread, and pilot length. Theoretical and simulation results indicate that secret key capacity in the slow fading channel is affected by frame-to-frame correlation and increasing transmission power or sampling period can suppress this effect.

In healthcare systems, the protection of the sensitive data of patient during sharing across multiple entities is crucial. Encrypting these data before outsourcing to the healthcare server is an effective method to protect data privacy. As a solution to protect data sharing within healthcare systems, identity-based encryption with equality test (IBEET) is an effective strategy that allows testing whether two ciphertexts encrypted from the same plaintext without certificate management. However, current IBEET schemes often lack of control over the trapdoors’ lifetime, i.e. the malicious server could use trapdoor to test whether two ciphertext generated after the trapdoor encrypted by the same plaintext all the time, potentially leaking sensitive data which the user are unwilling to see. To address this problem, we introduce a forward secure identity-based encryption with equality test (FS-IBEET) scheme. This new scheme prevents the malicious server from accessing useful information from newly encrypted files. We perform a thorough security analysis using the random oracle model under the q-BDHI assumption. Our experiments demonstrate that the performance of our scheme in comparison to other schemes.

The fully decoupled radio access network (FD-RAN) divides the base station (BS) into three components: 1) the uplink data BS (UBS); 2) the downlink data BS (DBS); and 3) the control BS (CBS) to achieve an ultra flexible network. FD-RAN can easily realize dynamic multi-BS cooperation to boost throughput and guarantee users’ quality of experience. However, as the number of cooperating BSs and concurrently accessed Internet of Things (IoT) devices increases, significant authentication and key agreement (AKA) overheads will be incurred, and the system becomes vulnerable to various security threats. This article proposes a novel lightweight AKA protocol that employs secret value sharing for secure key negotiation among BSs, user equipment and the core network (CN). Furthermore, multidevice of IoT access is optimized through aggregated message authentication codes with detecting functionality (AMAD) to efficiently manage and aggregate access requests. By utilizing interpolation polynomials and multiparty key agreement techniques, the proposed solution improves key negotiation efficiency while mitigating risks from man-in-the-middle (MitM) and Distributed Denial of Service (DDoS) attacks. Security analyses are conducted to validate the protocol while showcasing its superiority on computation, communication and transmission efficiency.

In EMR systems, numerous electronic medical records are usually uploaded to cloud servers for storage and data sharing. Public key authenticated encryption with keyword search (PAEKS) which can resist keyword guessing attacks (KGA) provides a feasible approach for data sharing and searching in EMR systems. Specifically, a PAEKS scheme requires the sender to use the receiver’s public key when encrypting, which restricts the generated ciphertext to be searchable by only one specified receiver. If a sender wants to share a data with multiple receivers, the sender has to repeatedly encrypt the data for multiple receivers using the public keys of different receivers. For the receiver, the same issue also exists in the trapdoor generation phase. Therefore, the traditional PAEKS is not suitable for EMR systems involving multiple senders and multiple receivers because of a large number of repeated computations. In this work, we present a practical scheme called multi-user keyword-field-free conjunctive searchable encryption (MU-KFFCSE). In order to achieve KGA-resistant searchable encryption between senders and receivers, we use receiver servers and sender servers such that the computation cost and communication cost of the ciphertext (trapdoor) generation phase are independent of the number of receivers (senders). The proposed scheme also supports flexible searchable encryption with keyword-field-free conjunctive utilizing polynomial technology, which not only increases the accuracy of search results, but also eliminates the limitation of fields. We prove that our scheme satisfies ciphertext indistinguishability (CI) and trapdoor indistinguishability (TI) security without random oracle. Theoretical analysis and experimental results show that our scheme is efficient in terms of computation cost and storage cost compared with existing schemes.

The increasing adoption of smart devices and cloud services, coupled with limitations in local computing and storage resources, prompts numerous users to transmit private data to cloud servers for processing. However, the transmission of sensitive data in plaintext form raises concerns regarding users’ privacy and security. To address these concerns, this study proposes an efficient privacy-preserving secure neural network inference scheme based on homomorphic encryption and secure multi-party computation, which ensures the privacy of both the user and the cloud server while enabling fast and accurate ciphertext inference. First, we divide the inference process into three stages, including the merging stage for adjusting the network structure, the preprocessing stage for performing homomorphic computations, and the online stage for floating-point operations on the secret sharing of private data. Second, we propose an approach of merging network parameters, thereby reducing the cost of multiplication levels and decreasing both ciphertext–plaintext multiplication and addition operations. Finally, we propose a fast convolution algorithm to enhance computational efficiency. Compared with other state-of-the-art methods, our scheme reduces the linear operation time in the online stage by at least 11%, significantly reducing inference time and communication overhead.

As a crucial component of intelligent transportation systems, VANETs are essential for enhancing road safety and enabling efficient traffic management. To ensure secure communication, vehicles often use pseudonyms to protect their identity privacy. However, unconditional anonymity can hinder accountability, making it very necessary to provide conditional privacy protection for vehicles. The conditional privacy-preserving technology not only protects the identity privacy of legitimate vehicles, but also can trace the real identity of malicious vehicles. Some existing schemes lack conditional privacy protection or have large computation and communication costs, which makes them unsuitable for resource-constrained VANETs environments. Hence, we improve the current schnorr-based aggregate signature by eliminating bilinear pairing operations, optimizing the aggregation procedure for batch verification and propose a lightweight certificateless-based aggregate signature scheme (ECPP-CLAS) for VANETs. In our scheme, the aggregation enables multiple signatures to be compressed into an aggregated signature and verified simultaneously, thereby reducing communication consumption, trusted entity generates the pseudonym for the corresponding vehicle through special construction to meet the conditional privacy-preserving requirement. The security analysis and performance evaluation show that our proposed scheme can meet the expected security objectives and lightweight requirements.

Artificial Intelligence Generated Content (AIGC) has created a fertile ground for image steganography. Existing Coverless Image Steganography (CIS) methods rely on image semantics to encode secrets, transmitting stego images without embedding, inherently resisting steganalysis. However, constructing CIS Datasets (CISDs) for these methods demands excessive resources, making them impractical for communication. Moreover, achieving low cost and high security is unattainable under these conditions. Therefore, we propose a CIS method based on semantic-controlled text-to-image generation. Our method disguises users as typical AIGC community members utilizing mainstream black-box text-to-image generation with Stable Diffusion (SD). During pre-processing, plain prompts, derived from dialogues with a large language model, are divided into coded and uncoded prompts through our encryption process, where a secret key determines coded prompts. In communication, confusion prompts are selected from uncoded and coded prompts, excluding those determined by secrets. Subsequently, our stego shuffling process combines topic, secret, and confusion prompts to produce stego prompt sets. Diverse stego images maintaining visual topic consistency are generated from these sets using SD with generation seeds indicating transmission order. By introducing confusion prompts, our method is secure from recognition when revealing stego prompts. Experimental results demonstrate our method achieves low communication costs and enhances communication security.

Public Key Infrastructure (PKI) has gained widespread attention for ensuring the security and integrity of data communication. While existing PKI mainly supports digital signatures, it is lacking in crucial anonymity, leading to the leakage of a signer’s identity information. To alleviate the issue, ring signatures are a suitable choice to provide anonymity as they allow users to create their own rings without the need for an administrator. Unfortunately, the utilization of ring signatures in PKI may present compatibility challenges within the system. Thus, proposing a general mechanism to convert a standardized$\Sigma $-based signature to a ring signature is far-reaching. In this paper, we propose a general construction for converting$\Sigma $-based signatures into ring signatures. To achieve this, we first introduce a$\Sigma $-based general model, providing a general transformation to convert existing$\Sigma $-based signatures into a$\Sigma $-protocol form. Subsequently, we incorporate our redesigned one-out-of-many relation within our general model and proceed to devise ring signatures leveraging on one-out-of-many proofs. Furthermore, to reduce the signature size, we employ the Bulletproofs folding technique, enabling the attainment of logarithmic size ring signatures. To demonstrate the wide applicability of our general construction, we present four prominent signatures as case studies. Ultimately, we conduct a rigorous security analysis and benchmark experimental evaluation. The signing and verification times are 0.44 to 0.97 times and 0.27 to 0.91 times compared to other state-of-the-art schemes, respectively. Additionally, we exhibit the lowest signature size to date.

Image steganalysis has always been an important topic in the field of information security, and researchers have designed many excellent steganalysis models. However, the existing steganalysis models tend to construct a single path and increase the convolution kernels to reduce the size of feature maps, which is not comprehensive enough to extract the features and may boost the number of parameters. In addition, the single residual block stacking may pay attention to protecting stego signals and neglect the mining of hidden features. To address these issues, we propose a steganalysis model based on dual-path enhancement and fractal downsampling, which is suitable for both spatial and JPEG domains. The model reuses and strengthens noise residuals through two dual-path enhancement blocks, and designs a fractal downsampling block for downsampling at multiple levels, angles, and composition structures. The experimental results demonstrate that the proposed model achieves the best detection performance in both spatial and JPEG domains compared with other start-of-the-art methods. Besides, we design a series of ablation experiments to verify the rationality of each component.

Extremely large-scale antenna arrays (ELAA) require near-field spherical wave modeling due to the substantial increase in the number of antennas, which introduces new spatial dimensions to physical layer key generation (PLKG). We investigate multi-user PLKG in near-field environments, where a base station with an ELAA simultaneously generates secret keys with multiple users. We derive an analytical expression for the key rate (KR). By utilizing spatial dimensions of distance and angle in near-field environments, we apply eigenvalue decomposition and singular value decomposition to design precoding matrices to reduce interference among user equipments (UEs) and extract uncorrelated subchannels. Given that the KR is non-convex, we approximate it and optimize the precoding matrix to increase the KR. After precoding design, the KR depends on the transmit power allocated to the subchannels. Two optimization problems are formulated to further optimize transmit power allocation. The first problem focuses on maximizing the sum KR. We apply the Lagrange multiplier method to determine the optimal power allocation variables by searching the Lagrange multiplier. To reduce computational complexity, a supervised feedforward neural network (FNN) is designed to capture the relationship between the power allocation variables and the Lagrange multiplier. The second optimization problem focuses on KR fairness. By introducing a slack variable that is smaller than the KRs of all users, we use the CVX toolbox to find optimal power allocation variables that maximize this slack variable. To further reduce complexity, the Lagrange multiplier method offers an analytical solution for power allocation variables in terms of Lagrange multipliers determined by the slack variable in the high-power case. We employ a bisection algorithm to find the slack variable. Furthermore, we propose an FNN to map transmit power to the slack variable. Simulations demonstrate that the proposed methods efficiently leverage near-field effects for multi-user PLKG, reducing pilot overhead.

Computer vision (CV) applications empower various Internet of Things (IoT) scenarios. However, their advancements in image generation and manipulation tools make it increasingly easy to produce highly deceptive forged images, escalating the risk of image forgery. Cryptography-based methods can secure images but cannot support direct CV applications with compromised visual legibility. Existing generative adversarial network (GAN)-based steganography methods can effectively facilitate CV applications and image forgery prevention with high indistinguishability between stego and cover images. However, they are inefficient in resource-constrained IoT scenarios. Therefore, we propose a lightweight image forgery prevention scheme for IoT using GAN-based steganography. Our scheme embeds identity data within images. If forged, it fails to recover, triggering alerts. Our scheme can significantly improve efficiency with a lightweight generator designed by incorporating blueprint separable convolutions, sum connections and discrete wavelet transform while ensuring high effectiveness. Real-world IoT experimental results demonstrate this.

This paper proposes a highly secure and efficient certificateless authenticated key agreement (CL-AKA) scheme, which is particularly apt for deployment in vehicular access networks, as it improves not only communication but also computational efficiency in real-world scenarios where multiple vehicles concurrently access the internet via a limited number of base stations. The cornerstone of our CL-AKA scheme stems from an improved certificateless signature (CLS). Specifically, we re-design the key structure of CLS, allowing signers to locally maintain a single public key (instead of two public keys in most state-of-the-art works) as well as two private keys after key generation. With such a novel key structure, the proposed CLS facilitates pairing-free signature generation and verification and realizes efficient batch verification on the verifier’s end. Moreover, a signer only needs to disseminate one public key to verifiers, thus saving communication bandwidth. In addition to the advanced CLS, we develop a CL-AKA scheme that efficiently handles network access requests from multiple vehicles at base stations. To resist physical attacks and achieve highly secure key management, we further integrate the Physical Unclonable Function (PUF) into our scheme. Formal security proofs demonstrate that the proposed CL-AKA scheme is secure against conventional attacks and preserves common security properties such as forward secrecy and session key independence. Finally, we develop a proof-of-concept prototype and conduct extensive experiments to demonstrate the efficiency and practicality of our scheme.

Online auctions, which are widely used on Internet advertising platforms, reduce the participation costs for buyers and sellers, and promote the flow of tens of billions of dollars in the global economy. However, Internet advertising platforms tend to be monopolistic and adopt a sealed bidding model. Therefore, when price is the sole determinant of the winner, how to publicly verify the correctness of auction results without disclosing bidding information has become a challenge. To address these issues, we propose a fully homomorphic encryption (FHE)-based sealed-bid auction protocol with public verifiability atop cross-blockchain. Through an approximate comparison algorithm, the proof of the winner consists of${m} \,\, -1$(or 1) homomorphic ciphertexts, significantly reducing communication costs, where m represents the number of bidders. Thus, anyone can check the winner’s proof and complete the public verification of correctness. Moreover, this paper designs a cross-blockchain auction system model, breaking the monopoly of platforms, and proposes a distributed private key sharing method, which realizes the auditing function of the relay chain. Finally, we formalize the security model, and verify the correctness, public verifiability and privacy of our scheme. The off-chain time overhead and on-chain gas consumption demonstrate the strong practicability of our protocol in large-scale auctions.

The cyclical and continuous working characteristics ofInternet of Things(IoT) devices make a large amount of the same or similar data, which can significantly consume storage space. To solve this problem, various secure data deduplication schemes have been proposed. However, existing deduplication schemes only perform deduplication based on data similarity, ignoring the internal connection among devices, making the existing schemes not directly applicable to parallel and distributed scenarios like IoT. Furthermore, since secure data deduplication leads to multiple users sharing same encryption key, which may lead to security issues. To this end, we propose a device relationship-based IoT data deduplication scheme that fully considers the IoT data characteristics and devices internal connections. Specifically, we propose a device relationship prediction approach, which can obtain device collaborative relationships by clustering the topology of their communication graph, and classifies the data types based on device relationships to achieve data deduplication with different security levels. Then, we design a similarity-preserving encryption algorithm, so that the security level of encryption key is determined by the data type, ensuring the security of the deduplicated data. In addition, two different data deduplication methods, identical deduplication and similar deduplication, have been designed to meet the privacy requirement of different data types, improving the efficiency of deduplication while ensuring data privacy as much as possible. We evaluate the performance of our scheme using five real datasets, and the results show that our scheme has favorable results in terms of both deduplication performance and computational cost.

The power grid has transitioned into a network system, comprising increasingly large-scale, complex multi-intelligent devices, multi-sensors, and multi-control units. Concurrently, the rapid advancement of quantum computers presents significant challenges to cross-domain authentication based on traditional cryptographic systems due to decryption issues. To address the issue of sharing identity authentication information across different trust domains, this paper introduces a lattice-based cross-domain encryption transmission scheme. This scheme resists quantum attacks and facilitates cross-domain identity authentication by pre-storing public parameters in the verifier entity and constructing a quantified trust relationship model between nodes within the cross-domain mechanism. This approach further enhances the security of keys and the authentication efficiency between nodes.

With rapid advances in communication technology, a new access architecture of fully decoupled radio access network (FD-RAN) has been proposed. FD-RAN completely decouples the base station (BS) into uplink data base station (UBS), downlink data base station (DBS), and control base station (CBS). Different BSs handle the uplink and downlink data of the user plane, as well as control signaling, and facilitate communication needs through multi-BS cooperation. To ensure the security of multi-BS cooperation and user access, it becomes imperative to conduct key negotiations among multiple parties. However, as the number of simultaneously accessed BSs increases, the existing access security mechanism imposes excessive overhead in FD-RAN, compromising both access security and efficiency. Additionally, it becomes susceptible to distributed denial of service (DDoS) attacks launched by potential attackers. This paper introduces a lightweight authentication and key agreement (AKA) protocol based on secret value ($m_{i},\ n_{i}$) sharing technology to negotiate multi-BS group communication keys, which ensures access security in FD-RAN. By leveraging interpolation polynomial and multi-party key negotiation, the proposed protocol achieves efficient and cost-effective key negotiation on both the user and BS sides, which mitigates the risk of man-in-the-middle (MitM) and DDoS attacks. Security analysis and further evaluation show that the proposed scheme can resist various known attacks, and guarantee the computational and communication efficiency of key negotiation within the FD-RAN context.

Abstract A confidential reporting system (CRS) allows reporters to report concerns or problems in confidence without the fear of blame or reprisals. Nevertheless, privacy has been the primary concern of reporters. In this paper, we propose a privacy-preserving confidential reporting system with designated reporters (PPCRS-DR) to protect the privacy of reporters and the confidentiality of reports. Our PPCRS-DR provides the following interesting features: (1) for an event, an auditor can designate a reporter to report; (2) an auditor can neither see the report nor know the reporter’s identity from an encrypted report if the reporter is not the designated one; (3) when an auditor is unavailable, he/she can temporarily designate a delegatee to collect and review reports on behalf of him/her. We formalize both the definition and security model of our PPCRS-DR, and propose a concrete construction. Furthermore, the security of the proposed PPCRS-DR is formally proven. The implementation shows that it is efficient. The novelty is to implement flexible decryption delegation of CRSs and protect reporters’ privacy.

With the rise of the Internet of Things (IoT), IoT data analytics has gradually stepping into the spotlight of data mining. Existing research has primarily focused on enhancing the precision of IoT data mining, while the privacy protection aspects have not been fulfilled so far. The deployment of IoT data mining is contingent on the protection of data privacy and its economic worth to all parties. However, most existing IoT data mining research disregards economic benefits and lacks incentives, limiting its applicability. To address this issue, we provide a system for differential privacy-based IoT privacy-preserving data mining (IoT-PPDM) with dynamic incentive mechanism, and a formal economic model for IoT data mining is constructed. We utilized noncooperative game theory to simulate the multilateral interaction process in IoT data mining. To encourage participation from all parties, a dynamic incentive mechanism is designed to establish a balance between privacy protection and data mining requirements. In addition, we discuss the utility of all participants and theoretically validate the feasibility of IoT-PPDM. The experimental results show that IoT-PPDM with dynamic incentive mechanism can increase the benefits for all participants while avoiding irrational behavior of all parties.

Physical-layer key generation (PLKG) is an information-theoretic security technique aimed at addressing key distribution challenges among resource-constrained legitimate users in the Internet of Things (IoT). However, in quasi-static or blocked environments, key generation faces significant limitations owing to its low entropy. To overcome this challenge, we propose leveraging a simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS). In this study, we introduce a STAR-RIS-aided multiuser PLKG framework and formulate an optimization problem focused on minimizing the average bit disagreement ratio through optimal phase-shift matrix adjustments. Initially, we derive closed-form solutions for optimal phase shifts, simplifying the problem to depend solely on amplitude coefficients. We then employ a Newton-Raphson algorithm under the energy-splitting (ES) protocol and a traversal method under the mode-switching (MS) and time-switching (TS) protocols to solve the optimization problem. Simulation results confirm the efficacy of our proposed schemes by comparison with existing benchmark schemes.

Digital image have become the main source of human information acquisition and exchange, which is widely used in aerospace, biomedical and military fields. Therefore, to ensure the secure transmission of digital image, this paper proposes a secure spatio-temporal chaotic pseudorandom generator for image encryption is proposed. Firstly, we consider the potential impact of precision loss in digital circuits on the degradation of chaotic systems. Therefore, we employ the unscented Kalman filter (UKF) to assess accuracy loss in both Logistic, Sine and Chebyshev maps, which is compensated for by introducing perturbations into the spatio-temporal chaotic system. Secondly, we design new Sine maps and Chebyshev maps with time-varying delays to perturb the time dimension of the non-adjacent coupled lattice and improve the complexity and security of the chaotic system. In the end, we use the newly designed spatio-temporal chaotic system as a pseudo-random generator to design a new image encryption scheme. In this paper, we present a security proof for the newly proposed spatio-temporal chaotic system and image encryption scheme. Furthermore, security experiments demonstrate that the spatiotemporal chaotic system and image encryption scheme presented in this paper exhibit improved uniform distribution, absence of chaos degradation or predictability issues while offering randomness suitable for engineering applications.

With the development of the Industrial Internet of Things (IIoT), the scale of IIoT data is rapidly increasing, bringing significant challenges to existing data management systems. To tackle this issue, we propose a similarity-based secure deduplication for IIoT cloud management system, which can effectively balance the security and availability of IIoT data and minimize the storage cost. Concretely, we propose a similarity-based secure deduplication algorithm for IIoT (IIoT-SBSD) by designing a similarity-preserving tag (IIoT-Simhash). This algorithm can perform similarity deduplication over ciphertexts, thus reducing storage space while ensuring data security. Besides, we construct a parallelizable edge-based deduplication framework in which similarity comparison and deduplication operations are performed directly by edge nodes, significantly alleviating the transmission pressure. Additionally, we propose similarity-based proofs of ownership, S-PoWs, to mitigate the impact of data deduplication on the user's access to the IIoT data. Experimental results show that our method significantly reduces storage space and transmission bandwidth without increasing the computation burden.

To support secure data mining and privacy-preserving computation, partial access and selective computation on encrypted data are desirable. Functional encryption (FE) is a new paradigm of public-key encryption and allows authorized users to compute specific functions on encrypted data without knowing the data. However, in some FE schemes, a trusted central authority (CA) is required to generate secret keys for users according to the description of functions. In this paper, to reduce trust on the CA and protect users' privacy, a privacy-preserving decentralised FE for inner product (PPDFEIP) scheme is proposed where multiple authorities co-exist and work independently without any interaction. Especially, to resist collusion attacks, all secret keys of the same user are tied to his/her global identifier (GID), but authorities cannot know any information of the GID even if they collaborate. We formalize the definition and security model of our PPFEIP scheme, and propose a concrete construction. Furthermore, the proposed scheme is implemented and evaluated. Finally, the security of our PPDFEIP scheme is reduced to well-known complexity assumptions. The novelty is to reduce trust on the CA, protect users' privacy and enable authorized users to compute inner product on encrypted data without compromising confidentiality.

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximize the SKR, a joint design problem of the BS’s precoding matrix and the IRS’s phase shift vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimization to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

Physical layer key generation (PLKG) leverages wireless channels to produce secret keys for legitimate users. However, in millimetre-wave (mmWave) frequency bands, the presence of blockage significantly reduces the key rate (KR) of a PLKG system. To address this issue, we introduce reconfigurable intelligent surfaces (RISs) as a potential solution for constructing RIS-reflected channels, thereby enhancing the KR. Our study focuses on the beam-domain channel model and exploits the sparsity of mmWave bands to enhance the randomness of secret keys. To relieve pilot overhead in multi-user systems, we employ a compressed sensing (CS) algorithm to estimate angular information and propose a channel probing protocol with the full-array configuration for acquiring the beam-domain channel. We derive the analytical expressions for the KR in the case of full-array configuration. To optimize the KR, we design the phase shift and precoding vectors based on the obtained angular information. Furthermore, we employ a water-filling algorithm that relies on the Karush-Kuhn-Tucker (KKT) conditions to optimize power allocation for estimating the beam-domain channel with the same channel variance. When channel variances of the beam-domain channel differ, we design a deep-learning-based power allocation method for a more complex problem. What is more, we design a sub-array configuration scheme that exploits the difference in spatial angles between users to reduce pilot overhead and derive the analytical expression for the KR. Through extensive simulations, we demonstrate that our proposed PLKG schemes outperform existing methods.

Combining a fog layer to aggregate and process data for smart grids is a straightforward approach, yet it inevitably leads to trust issues and audition difficulty. Most existing data-sharing architectures did not consider the copious meter–fog communication and fog–cloud bulk data transmission simultaneously, or they relay the trustworthiness of the fog nodes on a single center. In this article, we design a meter–fog–cloud-blockchain data-sharing architecture for smart grids where the fog nodes are not only data relay stations but also semitrusted local data processing points, and the dual blockchains are responsible for device key management and data integrity audit, respectively. Moreover, a certificateless aggregate signcryption between fog and smart meters is designed, and the data sharing between servers is based on an authenticated key agreement mechanism to achieve optimal efficiency overall. Detailed security proofs are demonstrated and performance results show the practicality and efficiency of our data-sharing scheme.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. The receiver hardware impairments interfere with the feature extraction of transmitter impairments, but their effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system by employing adversarial training to learn the receiver-independent features. Moreover, when there are multiple receivers, collaborative inference are designed to enhance classification accuracy. Finally, we show how it is possible to leverage fine-tuning for further improvement with fewer collected signals. To validate the approach, we have conducted extensive experimental evaluation by applying the approach to a LoRaWAN case study involving ten LoRa devices and 20 software-defined radio (SDR) receivers. The results show that receiver-agnostic training enables the trained neural network to become robust to changes in receiver characteristics. The collaborative inference improves classification accuracy by up to 20% beyond a single-receiver RFFI system and fine-tuning can bring a 40% improvement for underperforming receivers. The system is further evaluated on a more practical testbed. By making additional use of online augmentation and multi-packet inference, the identification accuracy is improved from 50% to 90% at 10 dB.

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS's precoding matrix and the IRS's reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR.

Local differential privacy (LDP) collects user data while protecting user privacy and eliminating the need for a trusted data collector. Several LDP protocols have been proposed and deployed in real-world applications. Frequency estimation is a fundamental task in the LDP protocols, which enables more advanced tasks in data analytics. However, the existing LDP protocols amplify the added noise in estimating the frequencies and therefore do not achieve optimal performance in accuracy. This paper introduces a convolution framework to analyze and optimize the estimated frequencies of LDP protocols. The convolution framework can equivalently transform the original frequency estimation problem into a deconvolution problem with noise. We thus add the Wiener filter-based deconvolution algorithms to LDP protocols to estimate the frequency while suppressing the added noise. Experimental results on different real-world datasets demonstrate that our proposed algorithms can lead to significantly better accuracy for state-of-the-art LDP protocols by orders of magnitude for the smooth dataset. And these algorithms also work on non-smooth datasets, but only to a limited extent. Our code is available at https://github.com/SEUNICK/LDP.

This paper investigates a physical layer key generation (PLKG) scheme for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input multipleoutput (MIMO) systems. Unlike traditional PLKG which relies on channel state information (CSI), we exploit the virtual angles of departure (AoDs). To accurately estimate these angles, we propose a redundant transforming matrix-based compressive sampling matching pursuit (RTMCoSa) method. We then derive the secret key rate (SKR) of the RIS-aided mmWave system. Simulation results demonstrate that the RTMCoSa method outperforms existing orthogonal matching pursuit (OMP) methods in channel probing for angle information. What is more, the proposed key generation scheme surpasses traditional CSI-based methods in SKR when the SNR is low. And the SKR of our method maintains robust when the SNR decreases.

The one time pad (OTP) technique can provide information-theoretic security and physical layer key generation (PLKG) is a promising candidate for OTP. In the joint PLKG and OTP scheme, channel probing, information reconciliation and encrypted packet transmission all need to occupy channel time. In this paper, we propose a time slot allocation scheme for PLKG in OTP. We divide the process of OTP into three stages: channel probing, information reconciliation and encrypted packet transmission. Then, we analyze the secret key capacity and the time slot cost of the whole process. Under the premise of ensuring safety, we give a time slot reduction strategy to reduce time slot cost. Meanwhile, we propose a time slot allocation optimization algorithm to realize the strategy. Simulation results verify that our scheme can significantly reduce the time slot cost.

In quasi-static environments, the key generation rate (KGR) of wireless physical layer key generation (PLKG) can be greatly limited. In this paper, we aim to improve the KGR by reconfigurable intelligent surface (RIS) and jointly design the quantizer and phase shift matrix. We give a reconfiguration strategy able to quantify both real and imaginary parts at the same time and give its closed-form solution of the phase shift matrix. To reduce the bit disagreement ratio (BDR), we design a quantizer suitable for RIS-assisted PLKG which uses non-uniform quantization. To achieve high KGR while maintain low BDR, we analyze the BDR in different quantization levels and realize an adaptive quantization selection. Meanwhile, the simulation results verify that the KGR of the proposed scheme is more than twice that of the existing scheme under a certain BDR threshold.

Physical layer key generation (PLKG) exploits the distributed entropy source of wireless channels to generate secret keys for legitimate users. When the millimeter wave (mmWave) channel is blocked, reconfigurable intelligent surfaces (RISs) have emerged as a prospective approach to constructing reflected channels and improving the secret key rate (SKR). This paper investigates the key generation scheme for the RIS-aided mmWave system. We study the beam domain channel model and exploit the sparsity of mmWave bands to reduce the pilot overhead. We propose a channel probing method to acquire the reciprocal angular information and channel gains. To analyze the SKR, we investigate the channel covariance matrix of beam domain channels. We find that the channel gains of beams are uncorrelated which increases the randomness of secret keys. Considering an eavesdropper, we derive the analytical expressions of SKR when the eavesdropping channel has overlapping clusters with the legitimate channel. Simulations validate that the proposed PLKG scheme outperforms existing schemes.

Physical layer key generation (PLKG) is a promising technique to establish symmetric keys between resource-constrained legitimate users. However, PLKG suffers from a low key rate in harsh environments where channel randomness is limited. To address the problem, reconfigurable intelligent surfaces (RISs) are introduced to reshape the channels by controlling massive reflecting elements, which can provide more channel diversity. In this paper, we design a channel probing protocol to fully extract the randomness from the cascaded channel, i.e., the channels through reflecting elements. We derive the analytical expressions of the key rate and design a water-filling algorithm based on the Karush-Kuhn-Tucker (KKT) conditions to find the upper bound. To find the optimal precoding and phase shift matrices, we propose an algorithm based on the Grassmann manifold optimization methods. The system is evaluated in terms of the key rate, bit disagreement rate (BDR) and randomness. Simulation results show that our protocols significantly improve the key rate as compared to existing protocols. Compared to multiple-antennas systems without a RIS, our proposed method achieves an average 9.51 dB performance gain when the side length of an element is 1/4 wavelength and the Rician factor is 0 dB.

With the rapid development of Internet of Things (IoT), big data analytics (BDA) has gradually stepped into the spotlight of IoT research. However, existing research mostly focused on improving the efficiency of data mining, the wishes and profits of the participants in BDA are largely ignored. In real systems, it is always supposed that the IoT data owners accept the big data analytics in IoT (BDA-IoT), the theoretical modeling of BDA-IoT is needed to be constructed urgently. In this paper, aiming to provide theoretical modeling of the practical application of BDA-IoT, we prove the feasibility of participants voluntarily participating in BDA-IoT for the first time. Subsequently, a non-cooperative game theory model with incentive and payment mechanisms is constructed, and the multi-parties interaction process in BDA-IoT is simulated. The overall benefits of all participants are then discussed, the rationality and feasibility of our study are proved. Simulation results show the feasibility of our model in improving the benefits of all stakeholders in a non-cooperative game, and the best choice for all participants is to accept BDA-IoT.

Public clouds have drawn increasing attention from academia and industry due to their high computational and storage performance. Attribute-based encryption (ABE) is the most promising technology to simultaneously achieve confidentiality and fine-grained access control of the cloud-stored data. However, traditional ABE that relies on centralized authority faces several key management issues, such as the key escrow, key distribution, key tracking, key update, and heavy communication and computing overhead for users, which will cause security concerns and impede its widespread application. On the other hand, blockchain technology preserves distributed ledgers to ensure the immutability and transparency of data, which can further solve the security vulnerabilities caused by system centralization. This paper proposes a blockchain-assisted transformation method to solve all the key management problems mentioned above in ciphertext-policy ABE by utilizing technologies such as secret sharing protocols. In addition, our transformation method realizes two additional benefits: outsourced decryption and efficient user revocation, which are extremely valuable for practical implementations. We simulate a demonstration by adopting the most popular permissioned blockchain, Hyperledger Fabric. The security and efficiency analysis reveals that the scheme obtained from our transformation method can achieve replayable chosen-ciphertext security with extremely efficient decryption.

Patient-centered data management and sharing of personal health records (PHRs) are difficult to be realized as data is controlled by doctors/hospitals. In addition, security and privacy, oppressive costs, search and tracing unreliability, and complicated access authorization caused by traditional encryption severely hinder the widespread adoption of PHRs. To overcome these challenges, we propose a blockchain-backed data sharing framework for PHRs, where the blockchain achieves reliable search and tracing. Furthermore, we design a hybridblockchain-backedsearchableproxysigncryption scheme, namedBC-SPSC. Specifically, an identity-based proxy signature (IBPS) is utilized to perform the authorization from patients to doctors to achieve authentic patient-centricity, therefore the blockchain can relate data with associated patients and doctors during data tracing. Moreover, BC-SPSC supports two search modes. The first mode adopts attribute-based encryption with keyword-based search (SABE), where all legitimate users can implement searches, but only users whose attributes satisfy the access structure can successfully decrypt. By contrast, the second mode utilizes attribute-based searchable encryption (ABSE) to accomplish fine-grained authorization in both search and data access/decryption, that is, who can search is also constrained by data owners. Adequate performance comparisons and simulation experiments indicate significant advantages of the BC-SPSC scheme in storage and computation overheads.

The direct anonymous attestation (DAA) scheme is a promising authentication scheme in a single trust domain. However, the existing schemes fail to effectively solve the problem of mutual authentication of vehicle nodes between different trust domains. This paper designs a cross-domain remote DAA scheme combined with a mimic defense mechanism (CD-DAA-MD) for the Internet of Vehicles (IoV). Specifically, the trust chain is constructed, and the mimic defense mechanism is combined with a trusted auditor (TA) to meet the urgent needs of the security of the IoV. Furthermore, a dynamic heterogeneous redundancy structure (DHR) composed of multiple heterogeneous executors is established, where the heterogeneous redundancy between each heterogeneous executor is applied to effectively enhance the security of the TA and simultaneously strengthen the trustworthiness of the TA’s identity when participating in the cross-domain link We analogize the proof method that combines the random oracle model and the protocol judgment to evaluate the security issues at the protocol level, where a model based on the probability of heterogeneous executors being compromised is utilized. Simulation results show that our scheme effectively reduces the communication overhead without compromising security. The communication overheads of the TA-Join protocol in our scheme are reduced by about 15% compared with existing schemes. The efficiency of completing the entire cross-domain interactive authentication process is nearly 25% higher than the existing schemes.

With the development of IoT technology, smart grid has gradually replaced the traditional grid. Smart grid is convenient and fast. It can provide real-time residential electricity monitoring and forecasting, give users better electricity guidance and save a lot of labor costs. Smart meters send customers’ electricity consumption data to the gateway, which aggregates the data and then sends it to the electricity consumption control center. But in this process, there will be a security problem of leakage of customer’s electricity consumption data. Most of the current user data privacy protection collection schemes use homomorphic encryption and randomization techniques. However, some of these schemes require a trusted third-party entity, and some may cause significant computational overhead. Due to the limited computational resources of smart meters, these techniques may be impractical. In this paper, we propose a local differential privacy data aggregation protection scheme based on the idea of grouping perturbation of electricity consumption data according to data domains. Experiments show that our scheme can provide statistical estimates of electricity in the region while satisfying the privacy protection of customers’ electricity consumption data. Moreover, our scheme has small computational and communication overheads, which can meet the application requirements in practical scenarios.

Wireless body area networks (WBAN) enable ubiquitous monitoring of patients, which can change the future of healthcare services overwhelmingly. As the collected data of patients usually contain sensitive information, how to collect, transfer, store and share data securely and properly has become a concerning issue. Attribute-based encryption (ABE) can achieve data confidentiality and fine-grained access control simultaneously. Identity-based ring signature (IBRS) allows patients to prove their identity without leaking any extra (private) information. However, the heavy computational burden of ABE and IBRS is intolerable for most power-limited mobile devices, which account for a large proportion of WBAN devices. This paper combines the attribute-based online/offline encryption (ABOOE) and IBRS to achieve an outsourced online/offline hybrid signcryption ($O^{3}$HSC) scheme. As far as we know, this scheme is the first signcryption scheme that adopts IBRS and satisfies online/offline signcryption simultaneously.$O^{3}$HSC divides the key generation and signcryption into offline and online phases to increase the throughput of the central authority and save the power resources of mobile devices, respectively. Besides, outsourced decryption and public signature verification are also realized.$O^{3}\mathrm {HSC}$achieves security under CCA and CMIA, and the performance analysis shows that$O^{3}\mathrm {HSC}$is a lightweight and applicable scheme for WBAN.

Privacy protection and message authentication issues in VANETs have received great attention in academia. Many authentication schemes in VANETs have been proposed, but most of them are based on classical difficult problems such as factorization in RSA setting or Elliptic Curve setting and are therefore not quantum resistant. If a quantum computer becomes available in the next few decades, the security of these schemes will be at stake. This paper presents a vehicular lattice-based direct anonymous attestation (V-LDAA) scheme adopting an optimized signature scheme based on automorphism stability which achieves postquantum security. A distributed pseudonym update and vehicle revocation mechanism based on the lattice is introduced in this paper, which means vehicles can update their pseudonyms and revoke the identity certificate by themselves without the need for pseudonym resolutions or CRLs checking. Compared with the existing lattice-based attestation schemes in VANETs, computation costs during signing and verification operations in V-LDAA are no longer related to the number of users, which makes it suitable for large-scale VANETs. Security analysis shows that V-LDAA resists TPM theft attacks and provides users with user-controlled anonymity, user-controlled unlinkability, and unforgeability against quantum adversaries. Experimental results show that V-LDAA reduces the blind signature size by 18%. The speed of blind signing is increased by 30%, and blind verification operation is accelerated 3 times compared with the existing lattice-based direct anonymous attestation (LDAA) scheme.

In this study, a novel reversible data hiding (RDH) in encrypted domain scheme for coding channel based on sliding‐block segmentation and adaptive steganography is proposed. The proposed scheme enriches the residual information with as little additional encryption information as possible to improve the testing error rate of a steganalyser by sliding‐block segmentation with bit stream encryption. The specific encryption process effectively weakens the correlation between the adjacent pixels and minimises the size of key stream bits. The encryption key can be further embedded in the channel code stream before transmitted in the channel. Experimental analysis shows that the image encrypted by the proposed RDH scheme can achieve a peak‐signal‐to‐noise ratio of >50 dB, as the payload is 0.5 bits per pixel (bpp). In terms of security performance, compared with the state‐of‐the‐art methods, their method has a higher testing error rate when the steganalyser is utilised. Even if the payload is 0.5 bpp, the testing error rate is >0.25.

Aiming to mitigate the landslide hazard after event and to investigate the potential landslide over large area, the three-step landslide investigation strategy based on Synthetic Aperture Radar Interferometry (InSAR) is proposed, which includes (1) fast landslide mapping is calculated with small volume SAR data over specific landslide region on demand. (2) Pre-landslide deformation are precisely recovered with all archived SAR data to analyze the spatiotemporal landslide characteristics. (3) The potential landslides are fully investigated in the catchment scale with large volume SAR data. Sentinel-1A SAR data are continuously involved to give fast landslide monitoring and investigation over Baige landside and Jinsha River catchment.

Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data. Algorithms for the two tasks, such as normalizing flows and generative adversarial networks (GANs), are often developed independently. In this paper, we propose the concept of continuous-time flows (CTFs), a family of diffusion-based methods that are able to asymptotically approach a target distribution. Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees. Our framework includes distilling knowledge from a CTF for efficient inference, and learning an explicit energy-based distribution with CTFs for density estimation. Both tasks rely on a new technique for distribution matching within amortized learning. Experiments on various tasks demonstrate promising performance of the proposed CTF framework, compared to related techniques.

In wireless sensor and actor networks (WSAN), the sensor node that detects an event always uses flooding algorithm to discover the path to the mobile actor. Although the flooding algorithm has many advantages such as its simplicity and reliability, it performs poorly in energy efficiency and scalability, especially in a dense deployed network. In this paper, we propose to consider the flooding algorithm design as the coverage and connectivity problem: the data packet has to be broadcast by a set of connected nodes, while their transmission range has to cover the whole network to ensure that the mobile actor can receive the data packet. Based on this idea, we propose a new algorithm called Striped-Flooding based on the strip-based deployment pattern which has been proved to provide coverage and connectivity with minimum number of nodes. The Striped-Flooding uses the parameters of line spacing scaling factor and deviation to control the flooding process such that the deliver rate can be guaranteed. The analytical and numerical results prove that the Striped-Flooding algorithm has greatly improvement on the energy efficiency and scalability, especially in the dense deployed WSAN.

For data collection in large-scale wireless sensor networks (WSNs), dynamic clustering provides a scalable and energy-efficient solution, which uses cluster head (CH) rotation and cluster range assignment algorithms to balance the energy consumption. Nevertheless, most existing works consider the clustering and routing as two isolated issues, which is harmful to the connectivity and energy efficiency of the network. In this paper, we provide a detailed analysis on the relations between clustering and routing, and then propose a joint clustering and routing (JCR) protocol for reliable and efficient data collection in large-scale WSN. JCR adopts the backoff timer and gradient routing to generate connected and efficient intercluster topology with the constraint of maximum transmission range. The relations between clustering and routing in JCR are further exploited by theoretical and numerical analyses. The results show that the multihop routing in JCR may lead to the unbalanced CH selection. Then, the solution is provided to optimize the network lifetime by considering the gradient of one-hop neighbor nodes in the setting of backoff timer. Theoretical analysis and simulation results prove the connectivity and efficiency of the network topology generated by JCR.