Existing secret sharing-based secure aggregation protocols suffer from high overhead and inflexibility in handling dynamic clients. To address these issues, this paper introduces AnniMask, an efficient and dynamic secure aggregation protocol. By replacing conventional secret sharing with a double-point annihilation function for mask generation, AnniMask reduces the state-of-the-art aggregation overhead fromO(nlog2n) in computation andO(nlogn) in communication to linear. Moreover, the protocol achieves linear complexity for handling dynamic clients, representing a progressive improvement over previous approaches. Crucially, AnniMask remains robust under arbitrary client dropout rates, whereas the tolerance of existing masking schemes is fundamentally limited by their secret sharing threshold. Formal security analysis proves that AnniMask provides stronger resilience against collusion attacks while ensuring privacy and integrity. Comprehensive experimental results demonstrate that AnniMask achieves superior efficiency while maintaining high model accuracy, showcasing its robustness even under a client dropout rate as high as 90%.

The large-scale deployment of Internet of Things (IoT) devices continuously generates spatio-temporal sensing data associated with time and location, which promotes the demand for privacy-preserving range-aggregate queries over such data. However, existing range-aggregate queries for IoT data only support aggregate computations over a single dimension, and fail to perform verification over range query result and aggregate computation. In this paper, we present VeriMIRA, a privacy-preserving range-aggregate query system over spatio-temporal IoT data with multidimensional numerical attributes. Technically, for range queries over outsourced spatio-temporal IoT data, we introduce new primitives of spatio-temporal prefix encoding (STPE) and verifiable and searchable spatio-temporal EF-Tree (VSEF-Tree), and combine improved symmetric homomorphic encryption (iSHE).For aggregate computation over multidimensional attributes, we introduce a new primitive of multi-client functional encryption with verifiable aggregation for inner product (VA-MCFE). By employing VSEF-Tree and VA-MCFE, the results of both range query and aggregation computation can be efficiently verified. In addition, we present security analysis on the introduced new primitives and VeriMIRA. Finally, we conduct extensive experiments, and evaluate the performance of existing solutions and VeriMIRA over a real-world dataset, which demonstrates practical computation and communication overhead for spatio-temporal range-aggregate queries. With sacrificing only 20% overhead in the data outsourcing stage, VeriMIRA achieves 70% time savings over state-of-the-art range-aggregate query approaches at 20,000 aggregations, while providing range-aggregate verification property.

Transaction fraud detection (TFD) remains a substantial challenge in the digital economy. Feature extraction is crucial in TFD, as relying solely on raw transactional features fails to capture the complex and dynamic nature of fraudulent behavior. Existing methods primarily rely on manually aggregated features, failing to account for the evolving nature of fraud patterns. These limitations undermine the accuracy and adaptability of TFD systems. In this work, we propose a behavioral drift-aware feature extraction (BDFE) method designed to capture evolving fraud patterns by modeling behavioral drift and extracting new feature representations for TFD. BDFE consists of four synergistic modules. The first module is a feature extractor that encodes user transactional behaviors into new representations. The second and third modules are a historical behavior-aware classifier and a current behavior-aware classifier, which jointly guide the extractor to learn more discriminative features by capturing patterns from historical and current transactions, respectively. The fourth module is a behavior discriminator that distinguishes between historical and current behaviors. Through adversarial training, the discriminator encourages the feature extractor to extract unified representations that are invariant to behavioral shifts. This design enables BDFE to learn drift-resilient features, thus enhancing the detection of evolving fraud patterns. Extensive experiments are conducted on real-world transaction datasets and public ones. The results show that BDFE achieves improvements in average precision from 2.32% to 12.17%, in recall from 3.65% to 14.57%, in F1-score from 1.80% to 11.79%, and in G-mean from 2.05% to 8.17% over its peers. These gains demonstrate its superior feature extraction capability and its effectiveness in enhancing TFD performance.

The inherent heterogeneity and mobility of Multi access Edge Computing (MEC) necessitate security protocols that ensure instant connectivity while maintaining resilience against resource exhaustion. This paper presents NiIas, a non-interactive instant authentication and secure data delivery proto col. Unlike conventional protocols that require prior handshakes, NiIas enables immediate payload transmission without session resumption delays. The protocol leverages a multi-authorization identity-based cryptosystem to decentralize trust and eliminate certificate management overhead. Furthermore, NiIas employs an authenticate-before-decryption mechanism as a lightweight admission control. This design filters unauthorized traffic prior to decryption and effectively protects edge verifiers from denial of-service attacks. Rigorous security analysis formally establishes the protocol's cryptographic guarantees. Moreover, numerical simulations on resource-constrained devices and M/D/1 queuing theoretic analysis demonstrate that NiIas achieves superior availability and stability compared to state-of-the-art protocols.

Graph Neural Networks (GNNs) have marked significant impact in traffic state prediction, social recommendation, knowledge-aware question answering and so on. As more and more users move towards cloud computing, it has become a critical issue to unleash the power of GNNs while protecting the privacy in cloud environments. Specifically, the training data and inference data for GNNs need to be protected from being stolen by external adversaries. Meanwhile, the financial cost of cloud computing is another primary concern for users. Therefore, although existing studies have proposed privacy-preserving techniques for GNNs in cloud environments, their additional computational and communication overhead remain relatively high, causing high financial costs that limit their widespread adoption among users. To protect GNN privacy while lowering the additional financial costs, we introducePanther, a cost-effective privacy-preserving framework for GNN training and inference services in cloud environments. Technically,Pantherleverages four-party computation to asynchronously executing the secure array access protocol, and randomly pads the neighbor information of GNN nodes. We prove thatPanthercan protect privacy for both training and inference of GNN models. Our evaluation shows thatPantherreduces the training and inference time by an average of 75.28% and 82.80%, respectively, and communication overhead by an average of 52.61% and 50.26% compared with the state-of-the-art, which is estimated to save an average of 55.05% and 59.00% in financial costs (based on on-demand pricing model) for the GNN training and inference process on Google Cloud Platform.

In the cloud-assisted Industrial Internet of Things (IIoT), flexible and secure data sharing promotes industry processes optimization and new products making. To enable selective data retrieval over categorized data collected by IoT devices in the encryption domain, key-aggregate searchable encryption (KASE) is adopted for group data sharing in IIoT due to efficient management of encryption keys. Nonetheless, existing solutions rarely consider user revocation property and suffered from the following limitations: 1) the user revocation needs interaction process between data owner and cloud and 2) the nonrevoked users may be accidentally revoked. Therefore, we propose a blockchain-based revocable KASE system (BC-RKASE) that achieves noninteractive user revocation and trusted revoked user management. Technically, the result is nontrivially obtained from revisiting the generation of aggregate keys and key updates via secret sharing, along with redesigning trapdoor adjustment and providing public verifiable proof for key updates by public blockchain. Besides formal security analysis, we conduct extensive experiments in a real cloud environment and thin IoT devices (Raspberry Pi) using a public IIoT dataset to confirm the practical performance of BC-RKASE. In particular, BC-RKASE outperforms state-of-the-art schemes with$13.4{\times {\sim }}15\times $time efficiency accelerating for data retrieval and runs more than$16\times $faster for user revocation.

Machine learning has profoundly influenced various aspects of our lives. However, privacy breaches have caused significant unease and concern among the general public. Preserving the privacy of sensitive data during the training and inference phases of machine learning is a key challenge. Cryptography-based privacy-preserving machine learning (crypto-based PPML) offers a viable solution to this challenge. In this article, we studied over 100 publications on crypto-based PPML frameworks published between 2016 and 2024, including 55 client-server architecture frameworks and 64 multi-party architecture frameworks. We provide a comprehensive overview of these frameworks, highlighting their features across various dimensions. Furthermore, we conduct an in-depth analysis, delving into scenarios, privacy goals, threat models, and optimization techniques that underpin these innovative solutions. We also discuss the challenges in the field of crypto-based PPML, including aspects of security and privacy , efficiency , and availability and usability . Finally, we offer an outlook on future research directions, aiming to provide valuable insights for both scholars and practitioners.

In the digital era, effective Transaction Fraud Detection (TFD) is essential to ensuring financial security. The considerable class imbalance, with legitimate transactions vastly outnumbering fraudulent ones, presents a significant challenge for TFD models to accurately identify fraudulent patterns. While existing sample-balancing strategies address class imbalance effectively in many contexts, they often fall short in TFD due to fraudsters’ sophisticated concealment tactics, which lead to pronounced behavioral overlap between fraudulent and legitimate transactions. In this paper, we introduce a novel Generative Adversarial Network-based Hybrid Sampling method (GANHS) to effectively address the class imbalance issue. GANHS employs a dual-discriminator generative adversarial network to generate synthetic samples that accurately reflect the characteristics of fraudulent activity, while an adaptive neighborhood-based undersampling technique refines these samples to minimize overlap with legitimate ones. This hybrid approach not only enhances the model’s ability to learn fraud patterns by generating high-quality samples but also improves its resilience against highly concealed fraudulent activities. Experiments on real-world and public datasets demonstrate that GANHS outperforms its competitive peers, with gains of 0.5%–8.7% in average$F_{1}$-Score and 1.0%–7.0% in G-mean, highlighting its strong potential for improving the reliability and effectiveness of TFD systems in complex, high-risk financial scenarios.

Unmanned aerial vehicle (UAV)-borne hyperspectral imagery has been applied in precision agriculture, owing to its high spatial and spectral resolution. Specifically, the high spatial resolution is conducive to revealing the textural characteristics of crops, while the high spectral resolution can depict detailed spectral differences among crops. In this study, we explored the potential of extended attribute profiles (EAP) in modeling the spectral-spatial characteristics of UAV-borne hyperspectral imagery for the precise crop classification. Specifically, two dimensionality reduction approaches, namely, principle component analysis (PCA) and independent component analysis (ICA), were performed on the hyperspectral image to extract components, based on which a series of EAP that measure different image characteristics are generated. To exploit the complementary information of different attributes, the extracted EAP were fused for the classification of crops using feature stacking (FS) and decision fusion (DF) strategies. Meanwhile, random forest (RF), support vector machine (SVM), and deep neural networks (DNN) were used as classifier for the precise classification of crops. Experiments conducted on the WHU-Hi dataset demonstrated that EAP exploited the spectral-spatial information of UAV-borne hyperspectral imagery and obtained satisfactory crop classification performance.

Credit card fraud detection (CCFD) is an important issue concerned by financial institutions. Existing methods generally employ aggregated or raw features as their representations to train their detection models. Yet such features tend to fall short of effectively exposing the characteristics of various frauds. In this work, we propose a spatial-temporal gated network (STGN) to automatically learn new informative transactional representations containing users’ transactional behavioral information for CCFD. A gated recurrent neural net unit is specifically constructed with a time-aware gate and location-aware gate to extract users’ spatial and temporal transactional behaviors. A spatial-temporal attention module is designed to expose the transaction motive of users in their historical transactional behaviors, which allows the proposed model to better extract the fraudulent characteristics from successive transactions with time and location information. A representation interaction module is offered to make rational decisions and learn compositive transactional representations. A real-world transaction dataset is used in experiments to verify the efficacy of the learned new representations. The results demonstrate that our proposed model outperforms the state-of-the-art ones, thus greatly advancing the field of CCFD.Note to Practitioners—The features of transaction records reflect the characteristics of users’ transactional behaviors. Therefore, effective features are critical for accurate CCFD. However, fraudsters often pretend to be legitimate users during transactions to deceive the CCFD system. As a result, fraudulent behaviors become concealed within legitimate ones, signifying that original features are inadequate for accurate CCFD. Thus, it is imperative for researchers and practitioners to extract new features that can well expose fraud characteristics. While existing methods employing some transaction aggregation strategies can spot certain fraudulent behaviors, they fail to clearly cluster all the anomalous behaviors and distinguish them from legitimate behaviors. Therefore, this work is driven by the urgent demand to extract new informative features for CCFD. Its primary focus is to unveil the aggregation of fraudulent transactional behaviors from both temporal and spatial perspectives, enabling more accurate CCFD. Specifically, this work introduces a new STGN model that automatically learns new transactional representations incorporating users’ transactional behavioral information for CCFD. By comprehensively considering the time interval and location interval of consecutive user transactions, we thoroughly reveal the temporal and spatial aggregation of fraudulent behavior, which provides valuable insights for CCFD practitioners: 1) employing features that integrate the behavioral characteristics of fraudsters instead of the original features can enhance the model’s capability to identify frauds, and 2) taking into account the time and location intervals of users’ consecutive historical transactions can better uncover the behavioral characteristics of fraudsters.

Secure cloud storage offers encrypted databases outsourcing service for resource-constrained clients, containing numerous tables with certain relations. Searchable symmetric encryption enables a client to search over its encrypted database on the cloud, while rarely considering queries over joins of tables. Join Cross-Tags (JXT) protocol (ASIACRYPT 2022) is thence presented that enables conjunctive queries over joins of tables, while neglecting arbitrary Boolean queries with disjunctive and conjunctive normal forms (DNF/CNF) in TWINSSE (PETS 2023). However, trivially combining JXT and TWINSSE for arbitrary DNF/CNF boolean queries over joins of tables seems infeasible due to: (i) no support for dis/conjunctive query with the same meta-keyword; (ii) returning inaccurate search results; (iii) incurring costly storage overhead. Therefore, we introduce TNT-QJ, a practical TwiN cross-Tag protocol for arbitrary boolean Query-Join over multi-tables. The result is technically obtained from revisiting TWINSSE’s framework via using s-term (the least frequent keyword) for the relation between a keyword and its meta-keyword, and non-trivially combined with JXT’s query-join approach for introducing a connective attributed in encryption tuples. In addition, we present a semi-full multi-fork searchable tree to store keyword information and reveal keyword containment relations, where the storage consumption is reduced from$\mathcal {O}(n^{3})$to$\mathcal {O}(n^{2})$. Finally, to clarify practical performance, we conduct extensive experiments on JXT and TNT-QJ using an open database in the HUAWEI cloud. Besides enabling disjunctive queries over joins of tables, TNT-QJ also runs$1.2\times $faster for conjunctive queries than JXT (with #keywords=2), which confirms rich features and practical efficiency.

Truck stop behavior plays a critical role in supporting powertrain electrification, which is key to reducing emissions in road freight. However, it is still unclear whether these stops meet the charging needs for electric trucks during intercity trips. This study aims to address this issue by analyzing intercity truck travel behavior based on trajectory data to inform battery electric truck (BET) technology design and charging infrastructure planning. The study proposes a data-driven truck trip identification framework based on GPS data to obtain truck stops and truck trip ends. Empirical analysis based on gasoline-enabled truck drivers’ travel behaviors in China shows that a 400 km battery range will meet almost 90% of the continuous driving needs of intercity truck trips. It is recommended that en-route charging stations be designed with a charging power of more than 564 kW, in line with the parking time characteristics of 80% of truck stops. The study also identifies potential power supply shortages on the intercity road power grid between 11:00-13:00, 18:00-19:00, and 21:00-1:00. By quantifying the number of facilities needed to provide BET drivers an energy-viable path to complete intercity trips without excessive detour costs, the study found that the travel behavior-based optimization model can reduce the level of truck charging detour to 5% by selecting just a small subset of alternative charging facility locations (55/302). Our study demonstrates that analyzing intercity truck travel behavior can provide critical insights for designing BET technology and charging infrastructure to support the transition to low-emission road freight.

With the development of Internet of Things (IoT) technology, smart mobile devices are widely used in daily life. The service providers always extensively collect data from users for training machine learning models in order to improve their accurate services. This also raises users’ concerns about data privacy and security. Federated learning, as an extension of centralized machine learning, allows several users working together to train a machine learning model on their own devices without sending their data to the centralized servers. However, existing research suggests that local models also contain privacy related to the users’ data. Unfortunately, the current privacy-preserving secure aggregation methods have either poor accuracy or high computational and communication costs in training process which can not afford by the IoT devices. In this work, we propose a federated learning framework supporting privacy-preserving data aggregation against external and internal attackers with lower computational and communication costs, which is suitable for the weak IoT devices. The scheme is also supporting aggregation with fault tolerance and dynamic user set even if a part of users leave the system in the training. Detailed security analysis and extensive experiments using a real dataset confirm the efficacy and efficiency of the proposed schemes.

This letter proposes a novel active learning (AL) framework that utilizes the information derived from multiscale superpixel maps for the classification of hyperspectral image. Considering that the nearby pixels with similar spectral properties tend to belong to the same class, we introduce the multiscale superpixel maps for the automatic labeling of the selected informative samples. Moreover, to exploit the multiscale characteristics of objects in the image, a hierarchical fusion approach is developed to integrate the spatial information provided by the superpixel maps into the classification result. To illustrate the effectiveness of the proposed AL framework, experiments on a series of hyperspectral images are conducted and analyzed. The results confirm the superiority of the proposed method compared to the other algorithms.

Ammonia nitrogen (NH4-N) can cause water eutrophication and is the main oxygen-consuming pollutant in water bodies. Remote sensing methods are more macroscopic than traditional measurement methods. However, due to the weak optical characteristics of NH4-N, traditional remote sensing data cannot meet the needs of NH4-N monitoring. In response to this problem, this paper attempts to use unmanned aerial vehicles (UAV) hyperspectral imagery combined with extreme gradient boosting (XGBoost)regression algorithm to quantitatively retrieve NH4-N in urban rivers. The results show that compared with the traditional empirical semi-empirical model, the accuracy of using the XGBoost algorithm to estimate the NH4-N in the water body is significantly improved, and is consistent with the field measurement.

Extracting roads from remote sensing images has both civilian and military value, such as GIS data update, road navigation, military command and so on. The existing road extraction methods are mainly based on fully convolutional neural networks, and have achieved the state-of-the-art results. However, the convolutional and deconvolutional forms of these methods destroy the completeness of the extracted road. In this paper, we present a novel road extraction method for extracting complete roads from high-resolution remote sensing imagery based on joint superpixel segmentation and Graph Convolutional Network(GCN). The proposed method retains more spatial detail information as well as effectively improves the integrity of the extracted roads. Experiments were conducted on the Massachusetts Road dataset to compare our proposed method to other commonly used full convolutional techniques for road extraction. The results demonstrated the validity and better performance of the proposed method.

Recently, hyperspectral anomaly detection methods based on low-rank representation(LRR) have been widely studied. However, the assumption of global low dimension of background may ignore the local structure information of hyperspectral image. In this paper, a novel LRR incorporating local spatial constraint method is proposed for hyperspectral anomaly detection. Different from LRR detector, the proposed method considers the spatial information based on the supe pixel in the background part. The proposed method and current state-of-the-art methods are tested on two sets of real data. The experimental results demonstrate that the proposed method is superior to the comparative method in terms of both colour map detection and quantitative evaluation.

UAV hyperspectral imagery has been widely applied in the fine classification of crops because of its high spectral resolution and high spatial resolution. As the crops in hyperspectral image show complicated characteristics, only the spectral information is insufficient to distinguish them. Therefore, we use multiple feature fusion method for fine classification of crops in UAV hyperspectral imagery. In our work, the GLCM texture, morphological profile, and endmember abundance feature, are extracted. Meanwhile, three fusion strategies, namely decision fusion, probability fusion, and stacking fusion, are employed to obtain the classification results. The experimental results illustrate the superiority of the multiple fusion approaches in the crop fine classification with hyperspectral imagery.

Secure cloud storage, which is an emerging cloud service, is designed to protect the confidentiality of outsourced data but also to provide flexible data access for cloud users whose data is out of physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is regarded as one of the most promising techniques that may be leveraged to secure the guarantee of the service. However, the use of CP-ABE may yield an inevitable security breach which is known as the misuse of access credential (i.e., decryption rights), due to the intrinsic “all-or-nothing” decryption feature of CP-ABE. In this paper, we investigate the two main cases of access credential misuse: one is on the semi-trusted authority side, and the other is on the side of cloud user. To mitigate the misuse, we propose the first accountable authority and revocable CP-ABE based cloud storage system with white-box traceability and auditing, referred to as CryptCloud±. We also present the security analysis and further demonstrate the utility of our system via experiments.

Deep learning (DL)-based hyperspectral classification primarily use "spatial patching" as preprocessing for incorporating local spatial information. This operation can help to promote classification accuracy but faces the following problems. First, it is difficult to determine the optimal size of spatial patches for different hyperspectral images (HSIs). Second, this operation only exploits spatial features locally but not globally. In this paper, we propose a novel spectral patching network (SPNet) with an end-to-end deep learning architecture for HSI classification. SPNet uses "spectral patching" and Atrous Spatial Pyramid Pooling (ASPP) module to fully preserve the local and global spatial contextual information of original HSIs. The experimental results with UAV-borne hyperspectral dataset demonstrate that the SPNet achieved state-of-the-art accuracy and visualization performance in.

The tailings reservoir is an inevitable part of the production of metal mines, and due to it is usually the accumulation of waste residue and waste water, the risk source of artificial debris flow with high potential energy has been formed and the environmental risk cannot be underestimated. Thus, it is an important disaster and environmental protection project for the mining enterprises. However, the existing methods cannot conduct a comprehensive disaster and environmental monitoring, considering the remote sensing technology is an effective method for the large-scale monitoring, thus, this global monitoring will be carried out through a novel UAV-ground hyper-spectral joint observation and processing, where the UAV hyper-spectral image, the ground hyper-spectral data of the water and waste residue, and water quality testing report will be used. In addition, the study area is in Xinjiang, the Belt and Road.

Homomorphic encryption always allows the linear arithmetic operations performed over the ciphertext and then returns equaling results as if the operations are taken over the original plaintext, which is always used for data aggregation in wireless sensor networks to keep the confidentiality of the data and cut down the transmission overhead of the ciphertext. In the marine sensor networks, sensors collect the multiple data such as temperature, salinity, pressure, and chlorophyll concentration in the ocean using a single hardware unit for further statistical analysis such as computing the mean and the variance and making regression analysis. However, directly using the homomorphic encryption cannot perform well in marine sensor data forwarding since the data need to turn to satellites or vessels as relays and be forwarded in multi-hop way. The data are not expected to be decrypted until arriving the final destinations. To tackle these issues, we design a secure data forwarding protocol based on the Paillier homomorphic encryption and multi-use proxy re-encryption. We also evaluate the computational overhead in term of the delay in the transmission and operation in various test beds. The experiment results show that the additional computational overhead brought by cryptographic operations could be minor and it has the merit of providing fixed data size passing through the multi-hop transmission.

Ciphertext-policy attribute-based encryption (CP-ABE) has been proposed to enable fine-grained access control on encrypted data for cloud storage service. In the context of CP-ABE, since the decryption privilege is shared by multiple users who have the same attributes, it is difficult to identify the original key owner when given an exposed key. This leaves the malicious cloud users a chance to leak their access credentials to outsourced data in clouds for profits without the risk of being caught, which severely damages data security. To address this problem, we add the property of traceability to the conventional CP-ABE. To catch people leaking their access credentials to outsourced data in clouds for profits effectively, in this paper, we first propose two kinds of non-interactive commitments for traitor tracing. Then we present a fully secure traceable CP-ABE system for cloud storage service from the proposed commitment. Our proposed commitments for traitor tracing may be of independent interest, as they are both pairing-friendly and homomorphic. We also provide extensive experimental results to confirm the feasibility and efficiency of the proposed solution.

As a sophisticated mechanism for secure finegrained access control over encrypted data, ciphertext-policy attribute-based encryption (CP-ABE) is one of the highly promising candidates for cloud computing applications. However, there exist two main long-lasting open problems of CP-ABE that may limit its wide deployment in commercial applications. One is that decryption yields expensive pairing cost which often grows with the increase of access policy size. The other is that one is granted access privilege for unlimited times as long as his attribute set satisfies the access policy of a given ciphertext. Such powerful access rights, which are provided by CP-ABE, may be undesirable in real-world applications (e.g., pay-as-youuse). To address the above drawbacks, in this paper, we propose a new notion called auditable σ-time outsourced CF-ABE, which is believed to be applicable to cloud computing. In our notion, expensive pairing operation incurred by decryption is offloaded to cloud and meanwhile, the correctness of the operation can be audited efficiently. Moreover, the notion provides σ-time fine-grained access control. The cloud service provider may limit a particular set of users to enjoy access privilege for at most σ times within a specified period. As of independent interest, the notion also captures key-leakage resistance. The leakage of a user's decryption key does not help a malicious third party in decrypting the ciphertexts belonging to the user. We design a concrete construction (satisfying our notion) in the key encapsulation mechanism setting based on Rouselakis and Waters (prime order) CP-ABE, and further present security and extensive experimental analysis to highlight the scalability and efficiency of our construction.

In recent years, the science of hyperspectral remote sensing has huge development in virtue of the integration of low-cost lightweight hyperspectral sensors and unmanned aerial vehicles (UAVs). As an alternative of manned aircraft, UAV has some unique advantages enabling the researchers acquire the hyperspectral images of their interest area flexibly and promptly. This review focuses on the recent developments of UAV borne hyperspectral remote sensing system, and gives an overview of the corresponding platforms, sensors, data acquisition, processing and current applications. Future challenges and research directions for UAV borne hyperspectral data are also addressed.

Outsourcing computation with verifiability is a merging notion in cloud computing, which enables lightweight clients to outsource costly computation tasks to the cloud and efficiently check the correctness of the result in the end. This advanced notion is more important in marine mobile computing since the oceangoing vessels are usually constrained with less storage and computation resources. In such a scenario, vessels always firstly outsource data set and perform a function computing over them or at first outsource computing functions and input data set into them. However, vessels may choose which delegation computation type to outsource, which generally depends on the actual circumstances. Hence, we propose a scalable verifiable outsourcing computation protocol ( SV-OC ) in marine cloud computing at first and extract a single-mode version of it ( SM-SV-OC ), where both protocols allow anyone who holds verification tokens to efficiently verify the computed result returned from cloud. In this way, the introduced “scalable” property lets vessels adjust the protocol to cope with different delegation situations in practice. We additionally prove both SV-OC and SM-SV-OC achieving selective soundness in the random oracle model and evaluate their performance in the end.

Since the technology of mobile cloud computing has brought a lot of benefits to information world, many applications in mobile devices based on cloud have emerged and boomed in the last years. According to the storage limitation, data owners would like to upload and further share the data through the cloud. Due to the safety requirements, mobile data owners are requested to provide credentials such as authentication tags along with the data. However, it is impossible to require mobile data owners to provide every authenticated computational results. The solution that signers’ privilege is outsourced to the cloud would be a promising way. To solve this problem, we propose three secure multi-entities delegated authentication protocols (MEDAPs) in mobile cloud computing, which enables the multiple mobile data owners to authorize a group designated cloud servers with the signing rights. The security of MEDAPs is constructed on three cryptographic primitive identity-based multi-proxy signature (IBMPS), identity-based proxy multi-signature (IBPMS), and identity-based multi-proxy multi-signature (IBMPMS), relied on the cubic residues, equaling to the integer factorization assumption. We also give the formal security proof under adaptively chosen message attacks and chosen identity/warrant attacks. Furthermore,compared with the pairing based protocol, MEDAPs are quite efficient and the communication overhead is nearly not a linear growth with the number of cloud servers. Copyright⃝c 2015 John Wiley & Sons, Ltd.

Ciphertext-policy attribute-based encryption (CP-ABE) enables fine-grained access control to the encrypted data for commercial applications. There has been significant progress in CP-ABE over the recent years because of two properties called traceability and large universe, greatly enriching the commercial applications of CP-ABE. Traceability is the ability of ABE to trace the malicious users or traitors who intentionally leak the partial or modified decryption keys for profits. Nevertheless, due to the nature of CP-ABE, it is difficult to identify the original key owner from an exposed key since the decryption privilege is shared by multiple users who have the same attributes. On the other hand, the property of large universe in ABE enlarges the practical applications by supporting flexible number of attributes. Several systems have been proposed to obtain either of the above properties. However, none of them achieve the two properties simultaneously in practice, which limits the commercial applications of CP-ABE to a certain extent. In this paper, we propose two practical large universe CP-ABE systems supporting white-box traceability. Compared with existing systems, both the two proposed systems have two advantages: 1) the number of attributes is not polynomially bounded and 2) malicious users who leak their decryption keys could be traced. Moreover, another remarkable advantage of the second proposed system is that the storage overhead for traitor tracing is constant, which are suitable for commercial applications.

Wireless mesh networks are promising means of providing network coverage to areas where infrastructure is difficult to install. However, little work has been done on leveraging security and transmission overheads among multicast members in a multihop wireless network. This paper proposes a novel architecture for multicast wireless mesh network by exploiting a cryptographic primitive, certificateless proxy re-encryption. The methodology we present reduces the transformation ciphertexts from ki¾?n down to k+n, where k and n denote the length of the best route from the sender to the target users and the number of the valid receivers in the group, respectively. The proxy re-encryption scheme used in our architecture is newly constructed. Our proxy re-encryption scheme is against adaptive chosen- ciphertext attacks in the standard model. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Digital signatures are one of the fundamental security primitives because they provide authenticity and nonrepudiation in the broadcast/multicast communication networks. However, the current broadcast/multicast authentication standards are vulnerable to signature flooding because excessive signature verification requests exhaust the computational resource of victims. The situation becomes worse in the case of the energy‐constrained networks such as wireless sensor networks and mobile ad hoc networks. As an essential variation of ordinary digital signature schemes, multisignature schemes enable a single compact signature to authenticate a message under a set of different signers. In this paper, we first propose an efficient identity‐based multisignature scheme with three interactive rounds under quadratic residue assumption, which equals to the large integer factoring assumption. By using the technique of quadratic residue‐based multiplicatively homomorphic equivocable commitment, an advanced identity‐based multisignature scheme is proposed to achieve to reduce the interactive round complexity to two rounds. We give the formal security proof that our schemes are existentially unforgeable under adaptively chosen message attacks and chosen identity attacks in the random oracle model. Compared with the previous work, our schemes are very efficient. In particular, our schemes are featured by the weak assumption and the efficient signing and verification procedures. Copyright © 2012 John Wiley & Sons, Ltd.

Delay/Disruption tolerant networks (DTNs) are self-organized wireless networks, where end-to-end network connectivity is not available and the data forwarding relies on the assumption that the intermediate nodes are ready to "store, carry and forward" messages in an opportunistic way. This assumption can be easily violated by the selfish nodes which may be unwilling to use their precious resources by serving as relays. Due to the unique network characteristics, incentive issue is extraordinarily challenging in DTNs. To tackle this issue, in this paper, we propose MobiGame, a user-centric reputation based incentive protocol for DTNs, which allows a node to manage its reputation evidence. For the fairness requirement, we define a game-theoretic framework to design reasonable costs and reward parameters in the MobiGame's bundle forwarding, which leads to a Perfect Bayesian Equilibrium. Performance simulations are given to demonstrate the security, effectiveness and efficiency.