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Abstract In federated learning, secret sharing is a key technology to maintain the privacy of participants’ local models. Moreover, with the rapid development of quantum computers, existing federated learning privacy protection schemes based on secret sharing will no longer be able to guarantee the data security of participants in the post-quantum era. In addition, existing privacy protection methods have the problem of high communication and computational overhead. Although the multi-stage secret sharing scheme proposed by Pilaram et al. is one of the effective solutions to the above problems, existing studies have proven the privacy leakage risk of this scheme. This paper firstly designs a new lattice-based multi-stage secret sharing scheme Improved-Pilaram to solve the security problem, which allows participants to use public vectors to reconstruct different secret values without changing the secret sharing. Based on Improved-Pilaram, this article proposes a post-quantum secure federated learning scheme PQSF. PQSF uses double masking technology to encrypt model parameters and achieves mask reconstruction through secret sharing. Since Improved-Pilaram is multi-stage, participants do not need to update their local secret shares frequently during training. Analysis and experimental results show that the PQSF proposed in this paper reduces the communication complexity between participants and reduces the computational overhead by about 20% compared with existing solutions.

With the intelligentization of the Internet of Vehicles (IoVs), Artificial Intelligence (AI) technology is becoming more and more essential, especially deep learning. Federated Deep Learning (FDL) is a novel distributed machine learning technology and is able to address the challenges like data security, privacy risks, and huge communication overheads from big raw data sets. However, FDL can only guarantee data security and privacy among multiple clients during data training. If the data sets stored locally in clients are corrupted, including being tampered with and lost, the training results of the FDL in intelligent IoVs must be negatively affected. In this paper, we are the first to design a secure data auditing protocol to guarantee the integrity and availability of data sets in FDL-empowered IoVs. Specifically, the cuckoo filter and Reed-Solomon codes are utilized to guarantee error tolerance, including efficient corrupted data locating and recovery. In addition, a novel data structure, Skip Hash Table (SHT) is designed to optimize data dynamics. Finally, we illustrate the security of the scheme with the Computational Diffie-Hellman (CDH) assumption on bilinear groups. Sufficient theoretical analyses and performance evaluations demonstrate the security and efficiency of our scheme for data sets in FDL-empowered IoVs.

In the context of the increasingly diversified blockchain technology, interoperability among heterogeneous blockchains has become key to further advancing this field. Existing cross-chain technologies, while facilitating data and asset exchange between different blockchains to some extent, have exposed issues such as insufficient security, low efficiency, and inconsistent standards. Consequently, these issues give rise to significant obstacles in terms of both scalability and seamless communication among blockchains within a multi-chain framework. To address this, this paper proposes an efficient method for cross-chain interaction in a multi-chain environment. Building upon the traditional sidechain model, this method employs smart contracts and hash time-locked contracts (HTLCs) to design a cross-chain interaction scheme. This approach decentralizes the execution of locking, verifying, and unlocking stages in cross-chain transactions, effectively avoiding centralization risks associated with third-party entities in the process. It also greatly enhances the efficiency of fund transfers between the main chain and sidechains, while ensuring the security of cross-chain transactions to some extent. Additionally, this paper innovatively proposes a cross-chain data interaction strategy. Through smart contracts on the main chain, data from sidechains can be uploaded, verified, and stored on the main chain, achieving convenient and efficient cross-chain data sharing. The contribution of this paper is the development of a decentralized protocol that coordinates the execution of cross-chain interactions without the need to trust external parties, thereby reducing the risk of centralization and enhancing security. Experimental results validate the effectiveness of our solution in increasing transaction security and efficiency, with significant improvements over existing models. Our experiments emphasize the system's ability to handle a variety of transaction scenarios with improved throughput and reduced latency, highlighting the practical applicability and scalability of our approach.

With the swift advancement of cloud computing and the Internet of Things (IoT), to address the issue of massive data storage, IoT devices opt to offload their data to cloud servers so as to alleviate the pressure of resident storage and computation. However, storing local data in an outsourced database is bound to face the danger of tampering. To handle the above problem, a verifiable database (VDB), which was initially suggested in 2011, has garnered sustained interest from researchers. The concept of VDB enables resource-limited clients to securely outsource extremely large databases to untrusted servers, where users can retrieve database records and modify them by allocating new values, and any attempts at tampering will be detected. This paper provides a systematic summary of VDB. First, a definition of VDB is given, along with correctness and security proofs. And the VDB based on commitment constructions is introduced separately, mainly divided into vector commitments and polynomial commitments. Then VDB schemes based on delegated polynomial functions are introduced, mainly in combination with Merkle trees and forward-secure symmetric searchable encryption. We then classify the current VDB schemes relying on four different assumptions. Besides, we classify the established VDB schemes built upon two different groups. Finally, we introduce the applications and future development of VDB. To our knowledge, this is the first VDB review paper to date.

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system.

In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring volatile organic compounds (VOCs) using a photoionization detector (PID). This study is based on the PID water-soil-gas VOC online monitor developed by this group, online monitoring of the concentration of different constituents of VOCs in different production enterprises of the petroleum and chemical industries in Shandong Province, with the concentration of the laboratory test, to build a relevant model. The correlation coefficient about the PID test concentration and the actual concentration correlation coefficient was obtained through the collection of a large number of data trainings. Based on the application of PID in VOC monitoring, the establishment of a PID high-precision calibration model is important for the precise monitoring of VOCs. In this paper, multiple quantitative analyses were conducted, based on SVM regression of PID response to VOC signals, to study the high-precision VOC monitoring method. To select the response signals of PID under different concentrations of environmental VOCs measured by the research group, first, the PID response to VOC signals was modeled using the support vector machine principle to verify the effect of traditional SVM regression. For the problem of raw data redundancy, calculate the time-domain and frequency-domain characteristics of the PID signal, and conduct the principal component analysis of the time-domain of the PID signal. In order to make the SVM regression more generalized and robust, the selection of kernel function parameters and penalty factor of SVM is optimized by genetic algorithm. By comparing the accuracy of PID calibration models such as PID signal feature extraction, SVM regression, and principal component analysis SVM regression, the superiority of photoionization detector using the signal feature extraction PCA-GA-SVM method to monitor VOCs is verified.

Industry 5.0 is seen as the next stage of the industrial revolution. However, the centralized management of the vast amount of data in Industry 5.0 poses significant security challenges. Because blockchain can provide a stable decentralized network for data in Industry 5.0, blockchain technology becomes an ideal solution for the security of data in Industry 5.0. However, with the explosion of data in Industry 5.0, the rapidly growing storage requirements are putting unbearable pressure on nodes and users, making it especially important to address the scalability of the blockchain. While using light nodes can alleviate storage pressure on nodes, it can also lead to data availability attacks. To address the above issues, this paper proposes a blockchain-based trusted data storage mechanism for Industry 5.0. The mechanism combines sharding and two-layer Merkle tree structures, and employs random low-density parity codes to code the tree. It enables light nodes to verify the authenticity of data and solve data availability attacks while improving the throughput of the blockchain. In addition, by using erasure codes to construct low-storage blockchain nodes, the network load of the nodes is effectively reduced, which solves the scalability issues of the blockchain.

Abstract With the rapid development of Industry 4.0, the data security of Industrial Internet of Things in the Industry 4.0 environment has received widespread attention. Blockchain has the characteristics of decentralization and tamper-proof. Therefore, it has a natural advantage in solving the data security problem of Industrial Internet of Things. However, current blockchain technologies face challenges in providing consistency, scalability and data security at the same time in Industrial Internet of Things. To address the scalability problem and data security problem of Industrial Internet of Things, this paper constructs a highly scalable data storage mechanism for Industrial Internet of Things based on coded sharding blockchain. The mechanism uses coded sharding technology for data processing to improve the fault tolerance and storage load of the blockchain to solve the scalability problem. Then a cryptographic accumulator-based data storage scheme is designed which connects the cryptographic accumulator with the sharding nodes to save storage overhead and solve the security problem of data storage and verification. Finally, the scheme is proved to be security and the performance of the scheme is evaluated.

Internet of things (IoT) is a technology that can collect the data sensed by the devices for the further real-time services. Using the technique of cloud computing to assist IoT devices in data storing can eliminate the disadvantage of the constrained local storage and computing capability. However, the complex network environment makes cloud servers vulnerable to attacks, and adversaries pretend to be legal IoT clients trying to access the cloud server. Hence, it is necessary to provide a mechanism of mutual authentication for the cloud system to enhance the storage security. In this paper, a secure mutual authentication is proposed for cloud-assisted IoT. Note that the technique of chameleon hash signature is used to construct the authentication. Moreover, the proposed scheme can provide storage checking with the assist of a fully-trusted entity, which highly improves the checking fairness and efficiency. Security analysis proves that the proposed scheme in this paper is correct. Performance analysis demonstrates that the proposed scheme can be performed with high efficiency.

Most of establishment-key protocols suffer from both session-specific temporary information attack and private key leakage issue.Therefore, a novel public key sharing-based symmetric encryption algorithm (PKS-SE) was proposed, in order to maintain the security of communication between smart meter and service providers.Based on bilinear mapping and super-singular curve, PKS-SE algorithm constructed the communication key between intelligent electricity meter and service provider, in order to avoid the key escrow problem.At the same time, PKS-SE algorithm reduced the number of required messages during the mutual authentication to only two messages, thus controlling the communication cost and operation cost of PKS-SE algorithm.Simulation results show that the proposed PKS-SE algorithm can effectively realize public key-sharing

Abstract Background Accumulated studies indicate that aberrant expression of long noncoding RNAs (lncRNAs) is associated with tumorigenesis and progression of colon cancer. In the present study, long intergenic non-protein coding RNA 1287 (LINC01287) was identified to up-regulate in colon cancer by transcriptome RNA-sequencing, but the exact function remained unclear. Methods Transcriptome RNA-sequencing was conducted to identify dysregulated lncRNAs. Expression of LINC01287 was evaluated by real-time quantitative PCR. The downstream targets of LINC01287 and miR-4500 were verified by luciferase reporter assay, pull down assay and western blot. The potential functions of LINC01287 were evaluated by cell viability assay, colony formation assay, soft agar assay, flow cytometry, transwell migration and invasion assay, and tumor xenograft growth in colon cancer cells. Results Our results indicated that LINC01287 was up-regulated in colon cancer patients. High LINC01287 expression was associated with advanced TNM stage, lymph node metastasis, distant metastasis and shorter overall survival. Knockdown of LINC01287 inhibited cell growth, colony formation in plates and soft agar, transwell cell migration and invasion, and epithelial-mesenchymal transition (EMT) of colon cancer cells, while LINC01287 overexpression had contrary effects. In addition, LINC01287 mediated MAP3K13 expression by sponging miR-4500, thus promoted NF-κB p65 phosphorylation. Restored MAP3K13 expression or miR-4500 knockdown partially abrogated the effects of silencing LINC01287 in colon cancer cells. Conclusion Our findings demonstrated that the LINC01287/miR-4500/MAP3K13 axis promoted progression of colon cancer. Therefore, LINC01287 might be a potential therapeutic target and prognostic marker for colon cancer patients.

In recent years, the National Climate Center has developed a dynamic downscaling prediction technology based on the Climate-Weather Research and Forecasting (CWRF) regional climate model and used it for summer precipitation prediction, but there are certain deviations, and it is difficult to predict more accurately. The CWRF model simulates the summer precipitation forecast data from 1996 to 2019 and uses a combination of dendrite net (DD) and artificial neural networks (ANNs) to conduct a comparative analysis of summer precipitation correction techniques. While summarizing the characteristics and current situation of summer precipitation in the whole country, the meteorological elements related to precipitation are analyzed. CWRF is used to simulate summer precipitation and actual observation precipitation data to establish a model to correct the precipitation. By comparing with the measured data of the ground station after quality control, the relevant evaluation index analysis is used to determine the best revised model. The results show that the correction effect based on the dendritic neural network algorithm is better than the CWRF historical return, in which, the anomaly correlation coefficient (ACC) and the temporal correlation coefficient (TCC) both increased by 0.1, the mean square error (MSE) dropped by about 26%, and the overall trend anomaly (Ps) test score was also improved, showing that the machine learning algorithms can correct the summer precipitation in the CWRF regional climate model to a certain extent and improve the accuracy of weather forecasts.

Blockchain, with its characteristics of non-tamperability and decentralization, has had a profound impact on various fields of society and has set off a boom in the research and application of blockchain technology. However, blockchain technology faces the problem of data availability attacks during its application, which greatly limits the scope and domain of blockchain applications. One of the most advantageous researches to address this problem is the scalable data availability solution that integrates coding theory design into the Merkle tree promise. Based on this scheme, this paper combines a zero-knowledge accumulator with higher efficiency and security with local repair coding, and proposes a data availability scheme with strong dataset privacy protection. The scheme first encodes the data block information on the blockchain to ensure tamper-proof data, and then uses a zero-knowledge accumulator to store the encoded data block information. Its main purpose is to use zero-knowledge property to protect the accumulation set information stored in the accumulator from being leaked and to ensure that no other information about the accumulation set is revealed during the data transmission. It fundamentally reduces the possibility of attackers generating fraudulent information by imitating block data and further resists data availability attacks.

Abstract Nephogram could provide important information for meteorological business, and nephogram recognition is a kind of challenge in the meteorological industry. In this paper, a selective neural network ensemble method based on K-means and Hadoop processing technology is proposed. This method combines the neural network, K-means clustering and AdaBoost in cloud environment. The experimental results show that the recognition precision of the method proposed by this paper is higher than that of traditional method in a stand-alone environment.

Due to the dietetic properties and remarkable nutritive value of rabbit meat, its industry is increasing rapidly. However, the association between circular RNAs, microRNAs, and proteins and muscle fiber type, and meat quality of rabbit is still unknown. Here, using deep sequencing and iTRAQ proteomics technologies we first identified 3159 circRNAs, 356 miRNAs, and 755 proteins in the longissimus dorsi tissues from Sichuan white (SCWrabs) and Germany great line ZIKA rabbits (ZIKArabs). Next, we identified 267 circRNAs, 3 miRNAs, and 29 proteins differentially expressed in the muscle tissues of SCWrabs and ZIKArabs. Interaction network analysis revealed some key regulation relationships between noncoding RNAs and proteins that might be associated with the muscle fiber type and meat quality of rabbit. Further, miRNA isoforms and gene variants identified in SCWrabs and ZIKArabs revealed some pathways and biological processes related to the muscle development. This is the first study of noncoding RNA and protein profiles for the two rabbit breeds. It provides a valuable resource for future studies in rabbits and will improve our understanding of the molecular regulation mechanisms in the muscle development of livestock. More importantly, the output of our study will benefit the researchers and producers in the rabbit breeding program.

With the rapid development of the Internet of Things (IoT) technology, the security problems it faces are increasingly prominent and have attracted much attention in industry and the academy. Traditional IoT architecture comes with security risks. Illegal intrusion of attackers into the network layer disrupts the availability of data. The untrusted transmission environment increases the difficulty of users sharing private data, and various outsourced computing and application requirements bring the risk of privacy leakage. Multi-key fully homomorphic encryption (MKFHE) realizes operations between ciphertexts under different key encryption and has great application potential. Since 2012, the first MKFHE scheme LTV12 has been extended from fully homomorphic encryption (FHE) and has ignited the enthusiasm of many cryptographic researchers due to its lattice-based security and quantum-resistant properties. According to its corresponding FHE scheme, the MKFHE schemes can be divided into four kinds: Gentry–Sahai–Water (GSW), number theory research unit (NTRU), Brakerski–Gentry–Vaikuntanathan (BGV), and FHE over the tour (TFHE). Efficiency and cost are urgent issues for MKFHE. New schemes are mainly improved versions of existing schemes. The improvements are mostly related to the four parts of MKFHE: security assumption, key generation, plaintext encryption, and ciphertext processing. We classified MKFHE schemes according to the improved partial schemes, and we present some improved techniques and the applications of MKFHE.

The concept of verifiable delay functions has received attention from researchers since it was first proposed in 2018. The applications of verifiable delay are also widespread in blockchain research, such as: computational timestamping, public random beacons, resource-efficient blockchains, and proofs of data replication. This paper introduces the concept of verifiable delay functions and systematically summarizes the types of verifiable delay functions. Firstly, the description and characteristics of verifiable delay functions are given, and weak verifiable delay functions, incremental verifiable delay functions, decodable verifiable delay functions, and trapdoor verifiable delay functions are introduced respectively. The construction of verifiable delay functions generally relies on two security assumptions: algebraic assumption or structural assumption. Then, the security assumptions of two different verifiable delay functions are described based on cryptography theory. Secondly, a post-quantum verifiable delay function based on super-singular isogeny is introduced. Finally, the paper summarizes the blockchain-related applications of verifiable delay functions.

Abstract Background Scabies, caused by infestation of the mite Sarcoptes scabiei, is one of the most severe ectoparasitic diseases in rabbits. Scabies seriously affects the commercial rabbit breeding, causing severe economic losses. Host resistance to S. scabiei is an important factor in further development of the rabbit industry. In the present study, we compared the host resistance to S. scabiei var. cuniculi of a new breed of domestic rabbit propagated by the Sichuan Animal Sciences Academy (QiXing rabbit, QX) compared with that of a traditional rabbit breed in the domestic rabbit industry (IRA rabbit, IRA). Methods Both QX and IRA rabbits were experimentally infested with live S. scabiei var. cuniculi mites for 48 h. Then, during the course of four-week experimental infestation period, the body weight of rabbits was recorded every two weeks for calculating body-weight variations in comparison to the non-infested control rabbits. Skin lesions in the foot area were assessed on weekly basis and serum samples were tested weekly for the estimation of changes in the total antibody levels (IgG, IgE and IgM). Moreover, DNA extracted from the blood samples was amplified for analysis of the genetic diversity in the major histocompatibility complex, class II, DQ Alpha (MHC-DQA) gene. Results Compared to the IRA rabbits, the QX rabbits showed a significantly higher (P

Edge computing is an important tool for smart computing, which brings convenience to data processing as well as security problems. In particular, the security of data storage under edge computing has become an obstacle to its widespread use. To solve the problem, the mechanism combing blockchain with regeneration coding is proposed to improve the security and reliability of stored data under edge computing. Our contribution is as follows. 1) According to the three-tier edge computing architecture and data security storage requirements, we proposed hybrid storage architecture and model specifically adapted to edge computing. 2) Making full use of the data storage advantages of edge network devices and cloud storage servers, we build a global blockchain in the cloud service layer and local blockchain is built on the terminals of the Internet of things. Moreover, the regeneration coding is utilized to further improve the reliability of data storage in blockchains. 3) Our scheme provides a mechanism for periodically validating hash values of data to ensure the integrity of data stored in global blockchain.

The domestic rabbit (Oryctolagus cuniculus f. domesticus) is a very important variety in biomedical research and agricultural animal breeding. Due to the different geographical areas in which rabbit breeds originated, and the long history of domestication/artificial breeding, rabbits have experienced strong selection pressure, which has shaped many traits of most rabbit varieties, such as color and weight. An efficient genome-wide single-nucleotide polymorphism (SNP) detection strategy is genotyping-by-sequencing (GBS), which has been widely used in many organisms. This study attempted to explore bi-allelic SNPs associated with fur color and weight-related traits using GBS in five rabbit breeds. The data consisted of a total 831,035 SNPs in 150 individuals from Californian rabbits (CF), German Zika rabbits (ZK), Qixing rabbits (QX), Sichuan grey rabbits (SG), and Sichuan white rabbits (SW). In addition, these five breeds of rabbits were obviously independent populations, with high genetic differentiation among breeds and low genetic diversity within breeds. A total of 32,144 SNP sites were identified by selective sweep among the different varieties. The genes that carried SNP loci in these selected regions were related to important traits (fur color and weight) and signal pathways, such as the MAPK/ERK signaling pathway and the Hippo signaling pathway. In addition, genes related to fur color and weight were identified, such as ASIPs, MITFs and KITs, ADCY3s, YAPs, FASs, and ACSL5s, and they had more SNP sites. The research offers the foundation for further exploration of molecular genetic markers of SNPs that are related to traits.

To better understand the effect of growing location on the phytochemical compounds and sensory properties of blueberry (Vaccinium spp.), here we investigated rabbiteye blueberry ‘Brightwell’ (Vaccinium ashei cv. ‘Brightwell’) grown in 10 locations of China. Significant differences in terms of total soluble solids, titratable acidity, flavonoids, phenols, as well as proanthocyanidins and anthocyanins, were found in the fruits (berries) of blueberry plants among the different sampled locations. Furthermore, their sensory properties, which evaluated by the electronic tongue method, also significantly differed among the 10 locations. The content of flavonoids, phenols, proanthocyanidins, and anthocyanins all had significant correlations with sensory properties, except that of aftertaste-astringency. A key finding to emerge was that blueberry plants grown at high altitude locations harbored a high content of total soluble solids, flavonoids, phenols, proanthocyanidins, and anthocyanins along with high scores for the sweetness. These results suggested cultivating blueberry at high altitude can produce fruit that not only possess pronounced beneficial health effects but also good taste.

Abstract Background Scabies is caused by Sarcoptes scabiei burrowing into the stratum corneum of the host’s skin and is detrimental to the health of humans and animals. Vaccines are an attractive alternative to replace the acaricides currently used in their control. Methods In the present study, the S. scabiei chitinase-like protein 5 (SsCLP5) was characterized and recombinant SsCLP5 (rSsCLP5) was evaluated as a candidate vaccine protein for anti-mite protection in rabbits. The expression, characterization and immunolocalization of SsCLP5 were examined. Vaccination experiments were performed on three test groups (n = 12 per group) immunized with purified rSsCLP5. Control groups (n = 12 per group) were immunized with PBS, QuilA saponin or empty vector protein. After challenge, the inflammatory reaction and skin lesions were graded and rSsCLP5 indirect ELISA was used to detect antibody IgG levels in serum samples at the time of vaccination and post-challenge. Results The results showed that rSsCLP5 had high immunoreactivity and immunogenicity. In S. scabiei, SsCLP5 had a wide distribution in the chewing mouthpart, legs and exoskeleton, especially the outer layer of the exoskeleton. Vaccination with rSsCLP5 resulted in 74.3% (26/35) of rabbits showing no detectable lesions after challenge with S. scabiei. Conclusions Our data demonstrate that rSsCLP5 is a promising candidate for a recombinant protein-based vaccine against S. scabiei. This study also provides a method for studying scabies vaccine using rabbit as an animal model and a basis for screening more effective candidate proteins.

Psoroptes ovis var. cuniculi is a common ectoparasite of the wild and domestic rabbits worldwide, which causes significant economic losses in commercial rabbit breeding. In China, the diagnosis of rabbits infested with P. ovis var. cuniculi currently relies on detection of clinical signs and Psoroptes mites in skin scrapings by microscopy examination. However, this method is not very efficient in detection of the low mite loads and/or sub-clinical infections. In the present study, we cloned and expressed an arginine kinase homolog gene from P. ovis var. cuniculi (Poc-AK) and used its recombinant protein rPoc-AK to develop an indirect enzyme-linked immunosorbent assay (iELISA) method for diagnosis of P. ovis var. cuniculi infestation in rabbits. The results showed that the rPoc-AK protein was ~61 kDa and had no signal peptide. The rPoc-AK-based iELISA achieved a 94.4% sensitivity and a 88.2% specificity, and was able to detect P. ovis var. cuniculi infection as early as the 1st week post-infection, prior to the appearance of clinical signs. Further field study showed 24.94% (66.33/266) clinically normal rabbits were seropositive with the highest and lowest seropositive rates for California (35.71%) and Belgian (15.14%), respectively. These results suggested that the rPoc-AK has potential as a diagnostic antigen for early P. ovis var. cuniculi infestation in rabbits.

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

Scabies is a highly contagious disease caused by Sarcoptes scabiei which burrows into stratum corneum of host’s skin. In this study, after optimizing vaccination schedule, a vaccination trial is comprised of three test groups of rabbits (n = 10/group) by immunization with (1) rSsCLP5; (2) rSsCLP12; or (3) a mixture of rSsCLP5 and rSsCLP12, three biological replicates groups (n = 10/group) and three control groups (n = 10/group). Levels of specific IgG, total IgE and cytokines in sera were detected and histopathologically analyzed as indicators of vaccine effects. The results showed that 85% (17/20) of rabbits exhibited no detectable skin lesions of S. scabiei infestation in mixed protein groups compared to single protein groups with 75% (15/20) and 70% (14/20), respectively. Moreover, the deworming rates of mixed groups are increased by 10%−20% compared with that of single groups. Each of six groups immunized with rSsCLP displayed significant increases of specific IgG, total IgE, IL-10, and TNF-α. The degree of skin damage in test groups also significantly lower than that of control groups. Thus, purified rSsCLP5 and rSsCLP12 subunit cocktail vaccine induced robust immune protection and could significantly decrease mite populations to reduce the direct transmission between rabbits.

In the IoT (Internet of Things) environment, smart homes, smart grids, and telematics constantly generate data with complex attributes. These data have low heterogeneity and poor interoperability, which brings difficulties to data management and value mining. The promising combination of blockchain and the Internet of things as BCoT (blockchain of things) can solve these problems. This paper introduces an innovative method DCOMB (dual combination Bloom filter) to firstly convert the computational power of bitcoin mining into the computational power of query. Furthermore, this article uses the DCOMB method to build blockchain-based IoT data query model. DCOMB can implement queries only through mining hash calculation. This model combines the data stream of the IoT with the timestamp of the blockchain, improving the interoperability of data and the versatility of the IoT database system. The experiment results show that the random reading performance of DCOMB query is higher than that of COMB (combination Bloom filter), and the error rate of DCOMB is lower. Meanwhile, both DCOMB and COMB query performance are better than MySQL (My Structured Query Language).

Internet of things (IoT) is used to provide real-time data collection and analysis of the target area by the cooperation of low-cost devices. The authentication towards multiple devices has become the research hot-spot considering of the requirement in real applications. Sensitivity and privacy of data have caused widespread concerns because low-cost devices are neither tamper-proof nor capable of performing public key cryptography efficiently. However, many researchers only focus on the authentication between two devices in the network. They ignore the authentication among group devices attached to one network. In this paper, we propose A Practical Group Authentication Scheme for Smart Devices in IoT. Note that one device group to be authenticated consists of a group of smart devices. The personal digital assistant (PDA) as the group leader controls authentication operations in its group. From the security analysis, our scheme can resist to various attacks. In addition, the performance analysis shows that our scheme has lower computational cost than the existing scheme.

Infestation of the epidermis with the highly contagious ectoparasite, Sarcoptes scabiei, causes scabies, which is characterized by intense itching, pruritus, and secondary infection. This condition affects humans, livestock, and wildlife worldwide, incurring large economic losses and reducing the quality of human life. In the present study, we cloned the alpha-enolase, a key enzyme in the glycolytic and gluconeogenesis pathways, from S. scabiei var. cuniculi, characterized it and produced soluble recombinant enolase protein (rSsc-eno). We determined the localization of Ssc-eno in isolated mites and mites in lesioned skin. The results showed that native enolase was intensely localized in the tegument of the mouthparts, the entire legs, and the whole mites' body, as well as in the gut and reproduction system. Interestingly, we found that native enolase was widely distributed in mites in lesioned skin, with obvious high protein intensity compared with isolated mites. Building on good immunoreactivity, an indirect enzyme-linked immunosorbent assay (ELISA) based on rSsc-eno showed 92% sensitivity and 95.8% specificity, compared with other indirect ELISA in this study, rSsc-eno based ELISA is better in detecting scabies in rabbits. Besides, this method can detect S. scabiei infection as early as 1 week post infection. Compared with other detection methods, such as traditional microscopic examination and recently published universal conventional PCR, rSsc-eno ELISA was more effective to detect early infection in rabbits. Additionally, in vitro incubation experiments demonstrated the concentration-dependent acaricidal activity of rabbit anti-rSsc-eno sera against larval mites, suggested its potential as a vaccine candidate.

Edge computing provides a unified platform for computing, networking, and storage resources, enabling data to be processed in a timely and efficient manner near the source. Thus, it has become the basic platform for industrial Internet of things (IIoT). However, computing′s unique features have also introduced new security problems. To solve the problem, in this paper, blockchain-based identity management combining access control mechanism is designed under edge computing. The self-certified cryptography is utilized to realize the registration and authentication of network entities. We bind the generated implicit certificate to its identity and construct the identity and certificate management mechanism based on blockchain. Secondly, an access control mechanism based on Bloom filter is designed and integrated with identity management. Moreover, for secure communication in resource-constrained edge devices, a lightweight secret key agreement protocol based on self-authenticated public key is constructed. These mechanisms work together to provide data security guarantees for IIoT such as authentication, auditability, and confidentiality.

Wireless body area networks (WBANs) are expected to play a vital role in the field of patient-health monitoring shortly. They provide a convenient way to collect patient data, but they also bring serious problems which are mainly reflected in the safe storage of the collected data. The privacy and security of data storage in WBAN devices cannot meet the needs of WBAN users. Therefore, this paper adopts blockchain technology to store data, which improves the security of the collected data. Moreover, a storage model based on blockchain in WBAN is proposed in our solution. However, blockchain storage brings new problems, for example, that the storage space of blockchain is small, and the stored content is open to unauthorized attackers. To solve the problems above, this paper proposed a sequential aggregate signature scheme with a designated verifier (DVSSA) to ensure that the user’s data can only be viewed by the designated person and to protect the privacy of the users of WBAN. In addition, the new signature scheme can also compress the size of the blockchain storage space.