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With the maturity and development of 5G field, Mobile Edge CrowdSensing (MECS), as an intelligent data collection paradigm, provides a broad prospect for various applications in IoT. However, sensing users as data uploaders lack a balance between data benefits and privacy threats, leading to conservative data uploads and low revenue or excessive uploads and privacy breaches. To solve this problem, a Dynamic Privacy Measurement and Protection (DPMP) framework is proposed based on differential privacy and reinforcement learning. Firstly, a DPM model is designed to quantify the amount of data privacy, and a calculation method for personalized privacy threshold of different users is also designed. Furthermore, a Dynamic Private sensing data Selection (DPS) algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds. Finally, theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection, in particular, the proposed DPMP framework has 63% and 23% higher training efficiency and data benefits, respectively, compared to the Monte Carlo algorithm.

Abstract Increasing evidence infers that some complex diseases are attributed to co-infection with multiple pathogens, such as shrimp white feces syndrome (WFS); however, there is a lack of experimental evidence to validate such causal link. This deficiency further impedes rational design of probiotics to elicit desired benefits to shrimp WFS resistance. Herein, we validated the causal roles of Vibrio fluvialis, V. coralliilyticus and V. tubiashii (in a ratio of 7:2:1) in shrimp WFS etiology, which fully satisfied Koch’s postulates. Correspondingly, we precisely designed four antagonistic strains: Ruegeria lacuscaerulensis, Nioella nitratireducens, Bacillus subtilis and Streptomyces euryhalinus in a ratio of 4:3:2:1, which efficiently guarded against WFS. Dietary supplementation of the probiotics stimulated beneficial gut populations, streptomycin, short chain fatty acids, taurine metabolism potentials, network stability, tight junction, and host selection, while reducing turnover rate and average variation degree of gut microbiota, thereby facilitating ecological and mechanical barriers against pathogens. Additionally, shrimp immune pathways, such as Fcγ R-mediated phagocytosis, Toll-like receptor and RIG-I-like receptor signaling pathways conferring immune barrier, were activated by probiotics supplementation. Collectively, we establish an updated framework for precisely validating co-infection with multiple pathogens and rationally designing antagonistic probiotics. Furthermore, our findings uncover the underlying beneficial mechanisms of designed probiotics from the probiotics–gut microbiome–host immunity axis.

The data privacy concern for mobile users (MUs) in mobile crowdsensing (MCS) has attracted significant attention. Federated Learning (FL) breaks down data silos, enabling MUs to train locally without revealing actual information. However, FL faces challenges from the selfish and malicious behavior of MUs, potentially harming the global model’s performance. To tackle the challenges, we propose a rational, reliable FL framework (RRFL) for MCS. Firstly, utilizing Euclidean distance and tracking malicious behavior frequency, we calculate risk scores for MUs and eliminate outlier updates. Secondly, we design a long-term, fair incentive mechanism, evaluating MUs’ comprehensive reputation based on risk scores from their historical sensing tasks. Rewards are allocated exclusively to consistently outstanding MUs, encouraging honest cooperation in MCS. Finally, we construct an extensive game with imperfect information, deriving the sequential equilibrium to validate the scheme’s reasonableness. Experimental verification on the MNIST dataset demonstrates the effectiveness and reliability of RRFL, with results indicating strong accuracy and overall cost performance. MCS participants achieve the desired maximum utility, with over a 50% reduction in detection costs compared to short-term FL incentive mechanisms in MCS.

Enterocytozoon hepatopenaei (EHP) is a parasite in shrimp farming. EHP mainly parasitizes the hepatopancreas of shrimp, causing slow growth, which severely restricts the economic income of shrimp farmers. To explore the pathogenic mechanism of EHP, the host subcellular construction, molecular biological characteristics, and mitochondrial condition of Litopenaeus vannamei were identified using transmission electron microscopy (TEM), real-time qPCR, an enzyme assay, and flow cytometry. The results showed that EHP spores, approximately 1 μm in size, were located on the cytoplasm of the hepatopancreas. The number of mitochondria increased significantly, and mitochondria morphology showed a condensed state in the high-concentration EHP-infected shrimp by TEM observation. In addition, there were some changes in mitochondrial potential, but apoptosis was not significantly different in the infected shrimp. The qPCR results showed that the gene expression levels of hexokinase and pyruvate kinase related to energy metabolism were both upregulated in the diseased L. vannamei. Enzymatic activity showed hexokinase and lactate dehydrogenase were significantly increased in the shrimp infected with EHP, indicating EHP infection can increase the glycolysis process and decrease the oxidative phosphorylation process of L. vannamei. Previous transcriptomic data analysis results also support this conclusion.

To solve the problem that intelligent devices equipped with deep reinforcement learning agents lack effective security data sharing mechanisms in the intelligent Internet of things, a general federated reinforcement learning (GenFedRL) framework was proposed for deep reinforcement learning agents.The joint training through model-sharing technology was realized by GenFedRL without the need to share the local private data of deep reinforcement learning agents.Each agent device’s data and computing resources could be effectively used without disclosing the privacy of its private data.To cope with the complexity of the real communication environment and meet the need to accelerate the training speed, a model-sharing mechanism based on synchronization and parallel was designed for GenFedRL.Combined with the model structure characteristics of common deep reinforcement learning algorithms, general federated reinforcement learning algorithm suitable for single network structure and multi-network structure was designed based on the FedAvg algorithm, respectively.Then, the model sharing mechanism among agents with the same network structure was implemented to protect the private data of various agents better.Simulation experiments show that common deep reinforcement learning algorithms still perform well in GenFedRL even in the harsh communication environment where most data nodes cannot participate in training.

Retaining wall is essential for stopes mining in two steps, for it can prevent the instability and collapse of backfill. In this study, taking the retaining wall of backfilled stope as the research object, a stability analysis method of retaining wall based on the close coupling of catastrophe theory and numerical analysis was proposed. First, by extracting the unit failure rate of the retaining wall from the numerical simulation results and fitting it with the mining depth, the functional expression between them was established. Second, the function relation was transformed into the normal form according to catastrophe theory, and the instability criterion of retaining wall was deduced. Furthermore, an effort was made to analyze the changing law of the state of retaining wall and calculate the critical span of stope, under different thickness conditions. On this basis, the application test of retaining wall was carried out by using this method. The results show that with the thickness decreasing, the values of splitting variables a and b show a reverse trend, which leads to the discriminant of instability criterion decreasing and turning from positive to negative, resulting in the collapse. Meanwhile, in order to ensure the stability, the wider the span of the stope, the thicker the retaining wall is required, and conversely, the thicker the retaining wall, the higher the adaptability to the span of stope. In addition, it can be found from the application test that instability was bound to occur with a thickness of 3 m, but the retaining wall with a thickness of 4 m maintained stable, which tended to be consistent with the analysis. Therefore, the stability analysis method proposed in this study provides a way to accurately evaluate the stability of the retaining wall and calculate the critical thickness of that, and its application value is expected to be further explored.

The PoW consensus algorithm has been proved to be incentive incompatible, existing computing centralization under high reward differences and slow convergence of forks in extreme cases.Based on this, an incentive-compatiblebased consensus algorithm SSPoW was proposed.By introducing local solutions to calculate the computing power aggregated on the block chain, the explicit quantification of computing power was used to speed up the convergence of the fork, thus satisfying the consistency of the blockchain.Incentive compatibility was achieved by improving the reward scheme, which reduced the problem of computing centralization caused by high reward differences.Simulation results prove that the proposed algorithm could effectively reduce the reward differences and is more efficient than the traditional PoW consensus algorithm, which has positive implications for improving system security and consensus efficiency.

Profile matching is a key feature in mobile social networking applications, where friends are recommended to requesters by comparing the similarity of attributes between them.However, users’ personal information is exposed to the risk of privacy disclosure in the process of profile matching.The existing solutions exist some issues such as inaccurate matching results or inability to meet users’ requirements for multi-dimensional privacy protection.Baesed on this, a privacy-preserving precise profile matching (P3M) scheme was proposed, which allowed users to flexibly set the matching range of attributes and distances according to their requirements.The Paillier encryption was utilized to ensure data security of users, and a secure dot product protocol was designed to achieve secure ciphertext comparison of user attributes and query ranges.The P3M realized multi-dimensional privacy-preserving of users including user feature attributes and location attributes.Finally, the correctness and security of P3M scheme were analyzed and proved in detail, and extensive experimental results verified the effectiveness and efficiency of P3M scheme.

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things (IoT) cloud to improve transmission efficiency, which has created a novel paradigm for improving the speed of common connected objects in IoT. However, although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services, security concerns, such as a lack of reliability and collusion attacks, still exist in the outsourcing computation. In this paper, we propose a reliable, anti-collusion outsourcing computation and verification protocol, which uses distributed storage solutions in response to the issue of centralized storage, leverages homomorphic encryption to deal with outsourcing computation and ensures data privacy. Moreover, we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum, which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion attacks. The results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure, reliable, and more effective compared with state-of-the-art approaches.

Utilizing mobile devices to collect location-related data in crowdsensing has the security problem of a location cheating attack.How to guarantee the quality of these sensitive data has become a challenge.In response to this, a location authentication scheme was proposed based on cooperation to detect malicious users who submit false locations.Firstly, users with mobile devices were used to generate location proofs for each other, and a location verification scheme was designed based on distance bounding protocols for obtaining new information by multiple collisions of the same random number, which ensured the user’s location should be unforgeable and achieved the purpose of improving the quality of collected data.In addition, a location proof cooperator select method based on region division and reputation model was proposed which effectively prevented collusion attack and solved the problem of concentration of equity.Meanwhile, the incentive mechanism based on reputation value and task completion time was designed to effectively improve the location proof cooperator efficiency of task completion.Finally, security analysis and performance evaluation indicate that the scheme has proved to be correctness and secure, and the advantages in computational cost and communication overhead.

Increasing evidence has supported dysbiosis in the faecal microbiome along control-adenoma-carcinoma sequence. In contrast, the data is lacking for in situ tumor bacterial community over colorectal cancer (CRC) progression, resulting in the uncertainties of identifying CRC-associated taxa and diagnosing the sequential CRC stages. Through comprehensive collection of benign polyps (BP, N = 45) and the tumors (N = 50) over the four CRC stages, we explored the dynamics of bacterial communities over CRC progression using amplicons sequencing. Canceration was the primarily factor governing the bacterial community, followed by the CRC stages. Besides confirming known CRC-associated taxa using differential abundance, we identified new CRC driver species based on their keystone features in NetShift, including Porphyromonas endodontalis, Ruminococcus torques and Odoribacter splanchnicus. Tumor environments were less selective for stable core community, resulting in heterogeneity in bacterial communities over CRC progression, as supported by higher average variation degree, lower occupancy and specificity compared with BP. Intriguingly, tumors could recruit beneficial taxa antagonizing CRC-associated pathogens at CRC initiation, a pattern known as “cry-for-help”. By distinguishing age- from CRC stage-associated taxa, the top 15 CRC stage-discriminatory taxa contributed an overall 87.4% accuracy in diagnosing BP and each CRC stage, in which no CRC patients were falsely diagnosed as BP. The accuracy of diagnosis model was unbiased by human age and gender. Collectively, our findings provide new CRC-associated taxa and updated interpretations for CRC carcinogenesis from an ecological perspective. Moving beyond stratifying case-control, the CRC-stage discriminatory taxa could add the diagnosis of BP and the four CRC stages, especially the patients with poor pathological feature and un-reproducibility between two observers.

In response to the existing problems that the federated learning might lead to the reduction of aggregation efficiency by handing the majority of irregular users and the leak of parameter privacy by adopting plaintext communication, a framework of privacy-preserving robust federated learning was proposed for ensuring the robustness of the irregular user based on the designed security division protocol.PPRFL could enable the model and its related information to aggregate in ciphertext on the edge server facilitate users to calculate the model reliability locally for reducing the additional communication overhead caused by the adoption of the security multiplication protocol in conventional methods, apart from lowering the high computational overhead resulted from homomorphic encryption with outsourcing computing to two edge servers.Based on this, user could calculate the loss value of the model through jointly using the verification sets issued by the edge server and that held locally after parameter updating of the local model.Then the model reliability could be dynamically updated as the model weight together with the historic information of the loss value.Further, the model weight was scaled under the guidance of prior knowledge, and the ciphertext model and ciphertext weight information are sent to the edge server to aggregate and update the global model parameters, ensuring that global model changes are contributed by high-quality data users, and improving the convergence speed.Through the security analysis of the Hybrid Argument model, the demonstration shows that PPRFL can effectively protect the privacy of model parameters and intermediate interaction parameters including user reliability.The experimental results show that the PPRFL scheme could still achieve the accuracy of 92% when all the participants in the federated aggregation task are irregular users, with the convergence efficiency 1.4 times higher than that of the PPFDL.Besides, the PPRFL scheme could still reach the accuracy of 89% when training data possessed by 80% of the users in the federated aggregation task were noise data, with the convergence efficiency 2.3 times higher than that of the PPFDL.

Aiming at the problems of data leakage of perceptual image and computational inefficiency of privacy-preserving classification framework in edge-side computing environment, a lightweight and privacy-preserving classification framework (PPCF) was proposed to supports encryption feature extraction and classification, and achieve the goal of data transmission and computing security under the collaborative classification process of edge nodes.Firstly, a series of secure computing protocols were designed based on additive secret sharing.Furthermore, two non-collusive edge servers were used to perform secure convolution, secure batch normalization, secure activation, secure pooling and other deep neural network computing layers to realize PPCF.Theoretical and security analysis indicate that PPCF has excellent accuracy and proved to be security.Actual performance evaluation show that PPCF can achieve the same classification accuracy as plaintext environment.At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPCF has obvious advantages in terms of computational cost and communication overhead.

To reduce the security implementation cost of the outsourcing data audit mechanism for the regenerating-coding-based distributed storage systems, an orthogonal algebraic coding method was put forward to construct a lightweight privacy-preserving audit scheme based on linear homomorphic authentication.The homomorphic authentication tags were generated with the orthogonalization between the file encoded data and the private secret key vector, and the privacy protection of the auditing response message was achieved by using the random masking that was constructed by randomizing the orthogonal basis vectors of the specific sub-vector of the user’s secret key.The work realized the effective integration of algebraic coding, privacy protection, and security auditing.Theoretical analysis shows that the proposed scheme can realize the information-theoretic security in the regenerating-coding-based storage applications.Compared with the existing works, the proposed scheme is of low computational complexity and communication overhead, and better performance advantages.

Under differential privacy, to solve the optimal publishing problem with multiple consistency constraints, an approximation method of multiple consistency constraints was proposed by the theoretical analysis of the principle of optimal consistency release.The main idea was to divide the consistency constraint problem into several consistency constraint sub-problems and then achieve the original problem's optimal consistency release by solving each consistency constraint sub-problem repeatedly and independently.The advantage was that after the consistency constraint problem divided, the sub-problems were often easier to solve, or the technology to achieve optimal and consistent release of sub-problems is quite mature.Therefore more complex differential privacy optimal release problem could be solved.After analysis, the approximation method's convergence was fully demonstrated, ensuring that any partition of consistency constrained sub-problems can always achieve the optimal consistency release of the original problem.Furthermore, taking the sales histogram publishing as an example, based on the approximation method of multiple consistency constraints, a parallel algorithm was designed with optimal consistency release under differential privacy.The experimental results show that the algorithm's efficiency is 400 times higher than that of the general solution, and the algorithm can process millions of large-scale data.

To tackle the problems of users not having intuitive cognition of the dynamic privacy changes contained in their sensing data in mobile edge crowdsensing (MECS) and lack of personalized privacy risk warning values in the data uploading stage, a dynamic privacy measurement (DPM) model was proposed.A structured representation of data obtained by a user participating in a sensing task was introduced and was transformed it into a numerical matrix.Then privacy attribute preference and timeliness were presented to quantify the dynamic privacy changes of data.With this, personalized privacy thresholds of users based on the numerical matrix were reasonably calculated.Finally, differential privacy processing was performed on the numerical matrix, and a model evaluation system was designed for the proposed model.The simulation results show that the DPM model was effective and practical.According to the given example, a data utility of approximately 0.7 can be achieved, and the degree of privacy protection can be significantly improved as the noise level increases, adapting to the MECS of IoT.

Deep Learning (DL) algorithms based on artificial neural networks have achieved remarkable success and are being extensively applied in a variety of application domains, ranging from image classification, automatic driving, natural language processing to medical diagnosis, credit risk assessment, intrusion detection. However, the privacy and security issues of DL have been revealed that the DL model can be stolen or reverse engineered, sensitive training data can be inferred, even a recognizable face image of the victim can be recovered. Besides, the recent works have found that the DL model is vulnerable to adversarial examples perturbed by imperceptible noised, which can lead the DL model to predict wrongly with high confidence. In this paper, we first briefly introduces the four types of attacks and privacy-preserving techniques in DL. We then review and summarize the attack and defense methods associated with DL privacy and security in recent years. To demonstrate that security threats really exist in the real world, we also reviewed the adversarial attacks under the physical condition. Finally, we discuss current challenges and open problems regarding privacy and security issues in DL.

Aiming at the problem of image privacy leakage and computing efficiency in edge environment,a lightweight and secure region proposal network (SecRPN) was proposed.A series of secure computing protocols were designed based on the additive secret sharing scheme.Two non-collusive edge servers cooperate to perform calculation modules such as secure feature processing,secure anchor transformation,secure bounding-box correction,and secure non-maximum suppression.Theoretical analysis guarantees the correctness and security of SecRPN.The actual performance evaluation shows that SecRPN is outstanding in the computational cost and communication overhead compared with the existing works.

Aiming at the information leakage problem in the process of deep neural network model calculation,a series of secure and efficient interactive computing protocols were designed between two non-collusive edge servers in combination with the additive secret sharing scheme.Since the nonlinear function cannot be split directly,a set of basic conversion protocols were proposed to realize the secure conversion of additive and multiplicative shares.After a few invokes,the power,comparison,exponential,logarithm,division and other low-level functions can be calculated securely.Due to the characteristics of data transfer and computation,the proposed protocols can be extended to array computation.Theoretical analysis ensures the correctness,efficiency and security of these protocols.The experimental results show that the error of these protocols is negligible,and the computational costs and communication overhead are better than the existing schemes.

Abstract Background Recently, increasing evidence supports that some complex diseases are not attributed to a given pathogen, but dysbiosis in the host intestinal microbiota (IM). The full intestinal ecosystem alterations, rather than a single pathogen, are associated with white feces syndrome (WFS), a globally severe non-infectious shrimp disease, while no experimental evidence to explore the causality. Herein, we conducted comprehensive metagenomic and metabolomic analysis, and intestinal microbiota transplantation (IMT) to investigate the causal relationship between IM dysbiosis and WFS. Results Compared to the Control shrimp, we found dramatically decreased microbial richness and diversity in WFS shrimp. Ten genera, such as Vibrio, Candidatus Bacilloplasma, Photobacterium, and Aeromonas, were overrepresented in WFS, whereas 11 genera, including Shewanella, Chitinibacter, and Rhodobacter were enriched in control. The divergent changes in these populations might contribute the observation that a decline of pathways conferring lipoic acid metabolism and mineral absorption in WFS. Meanwhile, some sorts of metabolites, especially lipids and organic acids, were found to be related to the IM alteration in WFS. Integrated with multiomics and IMT, we demonstrated that significant alterations in the community composition, functional potentials, and metabolites of IM were closely linked to shrimp WFS. The distinguished metabolites which were attributed to the IM dysbiosis were validated by feed-supplementary challenge. Both homogenous selection and heterogeneous selection process were less pronounced in WFS microbial community assembly. Notably, IMT shrimp from WFS donors eventually developed WFS clinical signs, while the dysbiotic IM can be recharacterized in recipient shrimp. Conclusions Collectively, our findings offer solid evidence of the causality between IM dysbiosis and shrimp WFS, which exemplify the ‘microecological Koch’s postulates’ (an intestinal microbiota dysbiosis, a disease) in disease etiology, and inspire our cogitation on etiology from an ecological perspective. Video abstract

As an emerging cryptographic primitive, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is suitable for the owner to share his personal health records (PHRs) in mobile healthcare systems (mHealth). Before deploying traditional CP-ABE in real mHealth applications, there are three concerns worth considering. First, the scale of pre-defined attribute universe is lack of scalability. Second, the plaintext access policy sent along with the ciphertext would leak the PHR owner's privacy. Third, it is difficult to identify the malicious user who intentionally disclosed his (partial or modified) private key. In this paper, we present HTAC, a fine-grained policy-hiding and traceable access control scheme for mHealth. In HTAC, the attribute universe is exponentially large and unbounded. Each attribute is expressed by an attribute name and an attribute value. In the encryption phase, the value is hidden in the ciphertext and only the generic attribute name is exposed. The malicious user will be precisely identified by searching the identity linked with the suspicious private key in an identity table. We further extend HTAC by removing the identity table and assigning more explicitly responsibility for the authority and the trace center. Then the storage overhead of tracing the malicious users is constant. The security analysis and performance comparison indicate that HTAC and the extended scheme are secure and practicable for real mHealth.

In mobile crowd sensing(MCS),attackers can reconstruct the social circle among sensing users,who use the social association information among sensing users and the correlation between the sensing user’s identity and sensing data to further attack a social alliance.In order to tackle this issue,a fog-aided identity privacy protection scheme is proposed.Firstly,two fog nodes are introduced which located at the edge of the sensing terminal.The one is task allocation center (TC) for handling the reasonable allocation of sensing tasks,and the other is data center (DC) for calculating sensing data.Furthermore,differential privacy is employed for preventing attackers from acquiring the specific social association weight of sensing users.Finally,in order to prevent attackers from obtaining the sensing users' identity information and sensing data at the same time,sensing users use different blind identities to communicate with TC and DC.Security analysis indicates that the proposed scheme can ensure the security of identity privacy information of sensing users in the process of completing sensing tasks.Experimental results show that the proposed scheme can protect the social association information between sensing users,and has a low delay.

Intensive studies have evaluated abiotic factors in shaping host gut microbiota. In contrast, little is known on how and to what extent abiotic (geochemical variables) and biotic (i.e., surrounding microbes, younger shrimp, and age) factors assemble the gut microbiota over shrimp ontogeny. Considering the functional importance of gut microbiota in improving host fitness, this knowledge is fundamental to sustain a desirable gut microbiota for a healthy aquaculture. Here, we characterized the successional rules of both the shrimp gut and rearing water bacterial communities over the entire shrimp farming. Both the gut and rearing water bacterial communities exhibited the time decay of similarity relationship, with significantly lower temporal turnover rate for the gut microbiota, which were primarily governed by shrimp age (days postlarval inoculation) and water pH. Gut commensals were primary sourced (averaged 60.3%) from their younger host, rather than surrounding bacterioplankton (19.1%). A structural equation model revealed that water salinity, pH, total phosphorus, and dissolve oxygen directly governed bacterioplankton communities but not for the gut microbiota. In addition, shrimp gut microbiota did not simply mirror the rearing bacterioplankton communities. The gut microbiota tended to be governed by variable selection over shrimp ontogeny, while the rearing bacterioplankton community was shaped by homogeneous selection. However, the determinism of rare and stochasticity of abundant subcommunities were consistent between shrimp gut and rearing water. These findings highlight the importance of independently interpreting host-associated and free-living communities, as well as their rare and abundant subcommunities for a comprehensive understanding of the ecological processes that govern microbial successions.

In this paper, we propose a novel secure role re-encryption system (SRRS), which is based on convergent encryption and the role re-encryption algorithm to prevent the privacy data leakage in cloud and it also achieves the authorized deduplication and satisfies the dynamic privilege updating and revoking. Meanwhile, our system supports ownership checking and achieves the proof of ownership for the authorized users efficiently. Specifically, we introduce a management center to handle with the authorized request and establish a role authorized tree (RAT) mapping the relationship of the roles and keys. With the convergent encryption algorithm and the role re-encryption technique, it can be guaranteed that only the authorized user who has the corresponding role re-encryption key can access the specific file without any data leakage. Through role re-encryption key updating and revoking, our system achieves the dynamic updating of the authorized user's privilege. Furthermore, we exploit the dynamic count filters (DCF) to implement the data updating and improve the retrieval of ownership verifying effectively. We conduct the security analysis and the simulation experiment to demonstrate the security and efficiency of our proposed system.

Ensuring multimedia content security is one of the main targets to solve multimedia information security problems and k-anonymous model is a classic and effective model to protect personal information when we publish multimedia data. The k-anonymous security with different k values has always been a hot topic of research, and there is no specific quantitative method for k-anonymous model security without considering any application scenarios or assumptions. In this paper, we give a quantitative method for the capability of attacker and defender under k-anonymous model. More specifically, we present a new general notion of limitation to provide a precise bound of attack and defense capability in a k-anonymous model. Firstly, we establish the attack channel and the defense channel from the point of the attacker's and the defender's view respectively. Moreover, we describe the capability of both attacking and defending sides by introducing the average mutual information, as well as the maximum value of the the average mutual information is the limitation of the capability of both sides, i.e. the channel capacity. Finally, according to the variation curve of the average mutual information amount, we compare the capability of the attacker and defender and analyze the effect of k value on it and get the best interval of k, that is, the value of k should be greater than 8 and less than 40.

In recent years, deploying Internet of Things (IoT) in electronic healthcare systems (EHS) has made great progress in healthcare detection. It is extremely important to reduce the cost of communication and ensure the authenticity and integrity of data. A linearly homomorphic signature scheme can solve the above problems. However, when the scale of EHS is too large, the transmission, storage and verification of signatures need a high cost. An aggregate signature can combine many signatures generated by many different users into a short one. Therefore, only one aggregate signature needs to be processed during verification, transmission and storage. Combining the advantages of aggregate signature and linearly homomorphic signature, this paper proposes an aggregate signature scheme based on a linearly homomorphic signature for EHS, which has both linear homomorphism and aggregation, and realizes double data compression. Moreover, our scheme can resist a potential real attack, named a coalition attack. The security of this scheme is rigorously demonstrated based on the computational Diffie–Hellman assumption in the random oracle model.

Atmospheric CO2 concentration is increasing, largely due to anthropogenic activities. Previous studies of individual free-air CO2 enrichment (FACE) experimental sites have shown significant impacts of elevated CO2 (eCO2) on soil microbial communities; however, no common microbial response patterns have yet emerged, challenging our ability to predict ecosystem functioning and sustainability in the future eCO2 environment. Here we analyzed 66 soil microbial communities from five FACE sites, and showed common microbial response patterns to eCO2, especially for key functional genes involved in carbon and nitrogen fixation (e.g., pcc/acc for carbon fixation, nifH for nitrogen fixation), carbon decomposition (e.g., amyA and pulA for labile carbon decomposition, mnp and lcc for recalcitrant carbon decomposition), and greenhouse gas emissions (e.g., mcrA for methane production, norB for nitrous oxide production) across five FACE sites. Also, the relative abundance of those key genes was generally increased and directionally associated with increased biomass, soil carbon decomposition, and soil moisture. In addition, a further literature survey of more disparate FACE experimental sites indicated increased biomass, soil carbon decay, nitrogen fixation, methane and nitrous oxide emissions, plant and soil carbon and nitrogen under eCO2. A conceptual framework was developed to link commonly responsive functional genes with ecosystem processes, such as pcc/acc vs. soil carbon storage, amyA/pulA/mnp/lcc vs. soil carbon decomposition, and nifH vs. nitrogen availability, suggesting that such common responses of microbial functional genes may have the potential to predict ecosystem functioning and sustainability in the future eCO2 environment.

Mobile healthcare (mHealth) is an emerging technology which facilitates the share of personal health records (PHR),however,it also brings the risk of the security and privacy of PHR.Attribute-based encryption (ABE) is regarded as a new cryptology to enhance fine-grained access control over encrypted data.However,existing attribute-based mHealth systems either lack of efficient traceable approach,or support only single authority.A traceable multi-authority attribute-based access control mHealth scheme was proposed,which was constructed over composite order groups and supports any monotonic access structures described by linear secret sharing scheme (LSSS).The adaptive security was proved under subgroup decisional assumptions.The traceability was proved under k-strong Diffie-Hellman (k-SDH) assumption.The performance analysis indicates that the proposed scheme is efficient and available.

The rapid development of cloud computing and big data technology brings prople to enter the era of big data,more and more enterprises and individuals outsource their data to the cloud service providers.The explosive growth of data and data replicas as well as the increasing management overhead bring a big challenge to the cloud storage space.Meanwhile,some serious issues such as the privacy disclosure,authorized access,secure deduplication,rekeying and permission revocation should also be taken into account.In order to address these problems,a role-based symmetric encryption algorithm was proposed,which established a mapping relation between roles and role keys.Moreover,a secure deduplication scheme was proposed via role-based symmetric encryption to achieve both the privacy protection and the authorized deduplication under the hierarchical architecture in the cloud computing environment.Furthermore,in the proposed scheme,the group key agreement protocol was utilized to achieve rekeying and permission revocation.Finally,the security analysis shows that the proposed role-based symmetric encryption algorithm is provably secure under the standard model,and the deduplication scheme can meet the security requirements.The performance analysis and experimental results indicate that the proposed scheme is effective and efficient.

Device-to-device (D2D) network utilizes various communication methods and regional resource sharing mechanisms, which, despite the efficient and effective communications, may lead to a variety of security threats. Therefore, without the assistance from the base station, effective managing regional resources and protecting private data on mobile devices have become a major challenge in D2D networks. In this paper, we propose a reliability-based central node election mechanism in a D2D network, where the attribute information is collected, normalized, and weight-summed to acquire the reliability of each mobile device. The central node can therefore be elected by sorting the reliability of all mobile devices. Furthermore, we propose a security protection mechanism of private data based on homomorphic encryption in a D2D network, where homomorphic encryption is employed to implement secure data aggregate of ciphertext in the elected central node. Finally, theoretical analyses and simulation experiment verify the superior effectiveness and efficiency of the proposed schemes.

In the user selection phase of mobile crowdsensing, most existing incentive mechanisms focus on either single-attribute selection or random selection, which possibly lead to serious consequences such as low user enthusiasm, decreased task completion rate, and increased cost of platform consumption. To tackle these issues, in this paper, we propose a novel incentive mechanism MAIM, which is based on multi-attribute user selection and participation intention analysis function in mobile crowdsensing. In this mechanism, the sensing platform employs the analytic hierarchy process to determine the weights of three attributes: participation threshold, cost, and reputation. The weight calculation results of each sensing user with respect to each attribute are then integrated to obtain the sorted weight of each user, with which the sensing platform will then obtain the optimal user set. From the users' perspective, they can autonomously decide whether to accept task processing requests, as enabled by the participation intention analysis function, thereby voiding the absolute authority and control of the sensing platform over users and achieving a two-way selection between the sensing platform and the sensing users. Furthermore, the sensing platform establishes a score-based reputation reward to inspire active performers and utilizes a punishment mechanism to overawe malicious vandals, which substantially helps activize enthusiasm of user participation and improve sensing data quality. Simulation results indicate that the proposed MAIM has significantly improved the sensing task completion ratio and the budget surplus ratio compared with the existing incentive mechanisms in mobile crowdsensing.

Because the solid-state drive (SSD) shows high access performance, it is usually integrated into existing hard disk drive (HDD)-based storage hierarchy to form HDD/SSD hybrid storage systems. To further improve the performance of HDD/SSD hybrid storage systems, data migration schemes have been put forward to migrate the sequential data between HDD and SSD. However, the existing schemes cannot be aware of dirty data in the buffer and then incur a large number of unnecessary page migrations. In this paper, we devise an efficient data migration scheme considering dying data for HDD/SSD hybrid storage systems. In this scheme, a new liveness state, called the dying state, is utilized to identify the live data, which will become dead shortly due to its corresponding dirty version in the buffer. To decrease the page migration count, this scheme uses the benefit-to-cost ratio to select a block for data migration and copies the up-to-date version of dying data into the free space instead of migrating the dying data. Our experimental results show that our proposed scheme can perform better than existing data migration schemes that are not aware of dying data under various benchmarks.

Soil bacterial microbial communities are important in the ecosystem function and succession of forests. Using high-throughput 16S rRNA gene sequencing and relative importance for linear regression, we explored how the structures of soil bacterial community were influenced by the environmental factors and restoration succession of secondary forests in the Miyaluo Mountains of western Sichuan, China. Using a space-for-time approach, field measurements and sampling were conducted in four stands at different stages of natural restoration. Results of distance-based multivariate analysis showed that soil pH, organic carbon, available phosphorus, and C/N ratio were the predominant environmental factors that collectively explained a 46.9% variation in the bacterial community structures. The community compositions were jointly controlled by the direct and indirect effects of the rehabilitation stages. The changes in soil environmental factors coincided with restoration succession could lead to the shifts in the relative abundance of different soil bacterial taxa. We screened 13 successional discriminant taxa that could quantitatively indicate the secondary succession subalpine stage. Collectively, our findings show that soil bacteria in different taxa are governed by different local soil variables and rehabilitation ages, which can lead to shifts in the relative abundance of different taxa in successional stages, ultimately changing the entire soil bacterial community with the succession of secondary forest.

Arsenic (As) contamination in soil and groundwater has become a serious problem to public health. To examine how microbial communities and functional genes respond to long-term arsenic contamination in vertical soil profile, soil samples were collected from the surface to the depth of 4 m (with an interval of 1 m) after 16-year arsenic downward infiltration. Integrating BioLog and functional gene microarray (GeoChip 3.0) technologies, we showed that microbial metabolic potential and diversity substantially decreased, and community structure was markedly distinct along the depth. Variations in microbial community functional genes, including genes responsible for As resistance, carbon and nitrogen cycling, phosphorus utilization and cytochrome c oxidases were detected. In particular, changes in community structures and activities were correlated with the biogeochemical features along the vertical soil profile when using the rbcL and nifH genes as biomarkers, evident for a gradual transition from aerobic to anaerobic lifestyles. The C/N showed marginally significant correlations with arsenic resistance (p = 0.069) and carbon cycling genes (p = 0.073), and significant correlation with nitrogen fixation genes (p = 0.024). The combination of C/N, NO(3) (-) and P showed the highest correlation (r = 0.779, p = 0.062) with the microbial community structure. Contradict to our hypotheses, a long-term arsenic downward infiltration was not the primary factor, while the spatial isolation and nutrient availability were the key forces in shaping the community structure. This study provides new insights about the heterogeneity of microbial community metabolic potential and future biodiversity preservation for arsenic bioremediation management.