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With the rapid expansion of the scale of multiprocessor systems, the importance of fault tolerance and fault diagnosis is increasingly concerned. For an interconnection network G, the h-component connectivity is an important indicator for evaluating the fault tolerance of G, which is defined as the minimum number of vertices whose deletion will disconnect G such that the remaining has at least h components. The h-component diagnosability, a newly precise diagnosis strategy to analyze the reliability of G, is the diagnosability under the condition that the number of components is at least h in the resulting graph after removing the faulty processor set. The t / k -diagnosability is a classic imprecise diagnosis strategy, which can identify up to t faulty processors by sacrificing accuracy to a certain extent, namely misdiagnosing at most k fault-free processors. In this paper, we investigate some combinatorial properties and the fault tolerance ability of the n-dimensional Bicube network, denoted by B Q n . Then we first prove that the ( h + 1 ) -component connectivity of B Q n is h ( n − 1 ) − h ( h − 1 ) 2 + 1 ( n ≥ 6 , 1 ≤ h ≤ n − 1 ). Moreover, we derive that the ( h + 1 ) -component diagnosability of B Q n is ( h + 1 ) ( n − 1 ) − h ( h + 1 ) 2 + 1 under the PMC model and MM⁎ model ( n ≥ 7 , 1 ≤ h ≤ n − 3 ). Furthermore, under the PMC model, we propose the t / k -diagnosis algorithm of B Q n and then derive that B Q n is [ ( k + 1 ) n − k ( k + 3 ) 2 ] / k -diagnosable ( n ≥ 6 , 0 ≤ k ≤ n − 2 ).

Dynamic Searchable Symmetric Encryption (DSSE) has gained increasing popularity as it enables users to perform both file updates and ciphertext retrieval over encrypted data . However, existing DSSE schemes still lead to privacy leakage ( e.g., forward and backward privacy) in the dynamic setting. Some forward and backward secure DSSE schemes have been proposed, but still cannot support the keyword shielding flexibly. To solve this challenging issue, we propose a Forward and Backward Authorized Keyword Search (FB-AKS) scheme with recoverable keyword shielding by using trapdoor permutations and puncturable encryption in this paper. Compared with existing forward and backward private schemes, FB-AKS achieves keyword authorization flexibly ( e.g., keyword shielding, keyword un-shielding). The formal security analysis proves that FB-AKS achieves forward and backward security. And extensive experiments demonstrate that FB-AKS has less computation and storage overheads .

A robust network intrusion detection system (NIDS) plays an important role in cyberspace security for protecting confidential systems from potential threats. In real world network, there exists complex correlations among the various types of network traffic information, which may be respectively attributed to different abnormal behaviors and should be make full utilized in NIDS. Regarding complex network traffic information, traditional learning based abnormal behavior detection methods can hardly meet the requirements of the real world network environment. Existing methods have not taken into account the impact of various modalities of data, and the mutual support among different data features. To address the concerns, this paper proposes a multi-dimensional feature fusion and stacking ensemble mechanism (MFFSEM), which can detect abnormal behaviors effectively. In order to accurately explore the connotation of traffic information, multiple basic feature datasets are established considering different aspects of traffic information such as time, space, and load. Then, considering the association and correlation among the basic feature datasets, multiple comprehensive feature datasets are set up to meet the requirements of real world abnormal behavior detection. In specific, stacking ensemble learning is conducted on multiple comprehensive feature datasets, and thus an effective multi-dimensional global anomaly detection model is accomplished. The experimental results on the dataset KDD Cup 99, NSL-KDD, UNSW-NB15, and CIC-IDS2017 have shown that MFFSEM significantly outperforms the basic and meta classifiers adopted in our method. Furthermore, its detection performance is superior to other well-known ensemble approaches.

Attribute-based encryption (ABE) and attribute-based keyword search (ABKS) facilitate fine-grained access and search control for cloud-assisted Industrial Internet of Things (IIoT). However, existing schemes suffer from the following drawbacks: (1) their computational overhead in data outsourcing and retrieval is exceptionally high; (2) they obtain wrong search results if one or more of the queried keywords are wrongly selected; (3) in most existing ABKS, the untrustworthiness of the cloud server is not taken into account, and the correctness of the search results is not verified; (4) existing verifiable ABKS schemes do not support search results verification without the main data, and verifiers have to first download the search results and then check their correctness. In this paper, we design a verifiable online/offline multi-keyword search (VMKS) scheme providing high-level solutions to the aforementioned problems. We use the healthcare setting as a case study, and we demonstrate how our VMKS can be deployed in cloud-assisted Healthcare IIoT (HealthIIoT). We prove the security of our VMKS in the standard model and under the hardness assumption of the decisional bilinear Diffie-Hellman (DBDH) problem. Empirical results demonstrate that our VMKS speeds up encryption and verification processes by more than 70% and 90%, respectively. Moreover, our scheme reduces the communication overhead in the verification phase by more than 80%.

Private set intersection (PSI) is a fundamental cryptographic protocol which has a wide range of applications. It enables two clients to compute the intersection of their private datasets without revealing non-matching elements. The advent of cloud computing drives the ambition to reduce computation and data management overhead by outsourcing such computations. However, since the cloud is not trustworthy, some cryptographic methods should be applied to maintain the confidentiality of datasets. But, in doing so, data owners may be excluded from access control on their outsourced datasets. Therefore, to control access rights and to interact with authorized users, they have to be online during the protocol. On the other hand, none of the existing cloud-based PSI schemes support fine-grained access control over outsourced datasets. This paper, for the first time, proposes an attribute-based private set intersection (AB-PSI) scheme providing fine-grained access control. AB-PSI allows a data owner to control intersection computations on its outsourced dataset by defining an access control policy. We also provide security definitions for an AB-PSI scheme and prove the security of our scheme in the standard model. We implement our scheme and report performance evaluation results.

With the widespread adoption of mobile devices in various IoT services, an increasing amount of personal sensitive data are stored in IoT devices using flash memory as storage medium. Personal sensitive data are subject to privacy leakage due to unauthorized access, accidentally loss or resale of IoT devices. To tackle this challenge, in this paper, we present a novel key derivation encryption (KDE) algorithm, which is then used to construct a secure data deletion (SDDK) scheme for IoT devices. Initially, we design a nodal key tree based on flash memory’s hierarchical structure, and present a KDE algorithm to generate data key for encrypting user’s sensitive data and simplify key management. Meanwhile, based on KDE, we propose an SDDK scheme by combining partial block erasure with key deletion to remove both the ciphertext and the key components after data expiration, thereby implementing secure data deletion on IoT devices. Furthermore, we formally describe the process of SDDK using a mathematical analysis model, and give an optimal solution to reduce the page transfer overhead by employing implicit enumeration analysis algorithm. Finally, security analysis shows that the KDE algorithm is provably secure and the SDDK scheme implements data privacy protection and secure deletion of invalid data. Performance analysis and experimental results indicate that the SDDK scheme is effective and efficient.

Resource-constrained users outsource the massive image data to the cloud to reduce storage and computation overhead locally, but security and privacy concerns seriously hinder the applications of outsourced image processing services. Besides, existing image processing solutions in the encrypted domain still bring high computation overhead, and even lead to characteristic loss. To this end, we propose a Privacy-Preserving Krawtchouk Moment (PPKM) feature extraction framework over encrypted image data by utilizing the Paillier cryptosystem. First, a mathematical framework for PPKM implementation and image reconstruction is presented in the encrypted domain. Then, the detailed expanding factor and upper bound analysis shows that plaintext Krawtchouk moment and plaintext image reconstruction can be realized over encrypted image with PPKM. Furthermore, the computation complexity of PPKM can be significantly reduced with the block-based parallel algorithm. Experimental results verify that the PPKM is feasible and acceptable in practice in terms of image reconstruction capability and image recognition accuracy.

Traitor tracing scheme is a paradigm to classify the users who illegal use of their decryption keys in cryptosystems. In the ciphertext-policy attribute-based cryptosystem, the decryption key usually contains the users’ attributes, while the real identities are hidden. The decryption key with hidden identities enables malicious users to intentionally leak decryption keys or embed the decryption keys in the decryption device to gain illegal profits with a little risk of being discovered. To mitigate this problem, the concept of blackbox traceability in the ciphertext-policy attribute-based scheme was proposed to identify the malicious user via observing the I/O streams of the decryption device. However, current solutions with blackbox traceability are impractical since either the composite-order group or the linear complexity of system users is required. In this article, we proposed a secure ciphertext-policy attribute-based set encryption scheme with the short decryption key. The proposed scheme bases on the prime-order group to improve computational performances and aggregates multiple attributes into a constant-size attribute set to reduce the costs of communication overheads. By applying our proposed scheme with fingerprint codes, we then give an instantiation of the ciphertext-policy attribute-based scheme with blackbox traceability. Our scheme is provably secure under various q-type assumptions.

With the flourishing of ubiquitous healthcare and cloud computing technologies, medical primary diagnosis system, which forms a critical capability to link big data analysis technologies with medical knowledge, has shown great potential in improving the quality of healthcare services . However, it still faces many severe challenges on both users’ medical privacy and intellectual property of healthcare service providers, which deters the wide adoption of medical primary diagnosis system. In this paper, we propose an effi c ient and priv a cy-preserving m edical p rimary diagno s is framework (CAMPS). Within CAMPS framework, the precise diagnosis models are outsourced to the cloud server in an encrypted manner, and users can access accurate medical primary diagnosis service timely without divulging their medical data. Specifically, based on partially decryption and secure comparison techniques, a special fast secure two-party vector dominance scheme over ciphertext is proposed, with which CAMPS achieves privacy preservation of user’s query and the diagnosis result, as well as the confidentiality of diagnosis models in the outsourced cloud server. Through extensive analysis, we show that CAMPS can ensure that users’ medical data and healthcare service provider’s diagnosis model are kept confidential, and has significantly reduce computation and communication overhead . In addition, performance evaluations via implementing CAMPS demonstrate its effectiveness in term of the real environment.

Proactive caching is a novel smart communication resource management method that can offer intelligent and economic networking services in the next generation cellular networks. In proactive caching, a common operation is using k-means to estimate content popularity. However, during the process, the base stations have to collect user’s location and content preference information to train a k-means model, which causes user privacy leakage. And current privacy-preserving k-means schemes usually suffer dramatic user quality of experience reduction, and cannot deal with the user dropout condition. Therefore, we propose a privacy-preserving federated k-means scheme (named PFK-means) for proactive caching in the next generation cellular networks. PFK-means is based on two privacy-preserving techniques, federated learning and secret sharing. In PFK-means, a suite of secret sharing protocols are designed to lightweight and efficient federated learning of k-means. These protocols allow privacy-preserving k-means training for proactive caching when there are dropout users. We seriously analyze the security of PFK-means and conduct comprehensive experiments to prove its security, effectiveness and efficiency. Through comparison, we can conclude that PFK-means outperforms other existing related schemes.

While decision-making task is critical in knowledge transfer, particularly from multi-source domains, existing knowledge transfer approaches are not generally designed to be privacy preserving. This has potential legal and financial implications, particularly in sensitive applications such as financial market forecasting. Therefore, in this paper, we propose a Privacy-preserving Multi-party Knowledge Transfer system (PMKT), based on decision trees, for financial market forecasting. Specifically, in PMKT, we leverage a cryptographic-based model sharing technique to securely outsource knowledge reflected in decision trees of multiple parties, and design a secure computation mechanism to facilitate privacy-preserving knowledge transfer. An encrypted user-submitted request from the target domain can also be sent to the cloud server for secure prediction. Also, the use of decision trees allows us to provide interpretability of the predictions. We then demonstrate how PMKT can achieve privacy guarantees, and empirically show that PMKT achieves accurate forecasting without compromising on accuracy.

With the development of cloud computing, many enterprises have been interested in outsourcing their data to cloud servers to decrease IT costs and rise capabilities of provided services. To afford confidentiality and fine-grained data access control, attribute-based encryption (ABE) was proposed and used in several cloud storage systems. However, scalability and flexibility in key delegation and user revocation mechanisms are primary issues in ABE systems. In this paper, we introduce the concept of a fully distributed revocable ciphertext-policy hierarchical ABE (FDR-CP-HABE) and design the first FDR-CP-HABE scheme. Our scheme offers a high level of flexibility and scalability in the key delegation and user revocation phases. Moreover, our scheme is efficient and provides lightweight computation in the decryption phase. Indeed, by exploiting a computation outsourcing technique, most of the operations are executed by the powerful cloud server, and very few computations are left to the users. Also, the storage cost on the user side is significantly decreased as compared to similar schemes. Furthermore, using the hardness assumption of DBDH problem, we prove that our scheme is adaptively secure in the standard model. Our security analyses and implementation results indicate that our scheme is efficient, secure, and scalable.

With the promotion of online medical pre-diagnosis system, more and more research has begun to pay attention to the issue of privacy, and existing privacy-preserving schemes are designed for single-label data. However, medical users may infect many different diseases at the same time, it is necessary to take multi-label instances into account. In this paper, we propose an efficient and privacy-preserving multi-level medical pre-diagnosis scheme, called CREDO, which based on multi-label k-nearest-neighbors (ML-kNN). With CREDO, medical users can ensure their sensitive health information secure, and service provider can provide high-efficiency service without revealing pre-diagnosis model data. Specifically, service provider first narrows down the scope of medical instances needed to be calculated based on k-means clustering, then provides service for medical users based on ML-kNN classification. The query vector is encrypted before being sent out and directly operated in the service provider, meanwhile, the pre-diagnosis result can only be achieved by the medical user. Through extensive analysis, we show that CREDO can resist multifarious known security threats, and has much lower computation complexity than the compared scheme. Moreover, performance evaluations based on a real medical dataset demonstrate that our proposed scheme is highly efficient in terms of computation and communication overhead.

In cloud era, it is necessary to store sensitive data in an encrypted form. This arises the interesting and challenging problem of searching on encrypted data. However, previous Public-key Encryption with Keyword Search (PEKS) inherently cannot resist against inside keyword guessing attacks. To alleviate this issue, recently Huang and Li proposed the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS), which requires the data sender not only encrypting a keyword using the receiver’s public key, but also authenticating it using his secret key. This paper first revisits HL-PAEKS security model and finds that it did not capture a realistic threat, called (outside) chosen multi-ciphertext attacks. That is, an outside adversary can decide whether two encrypted files share some identical keywords or not. To resolve this issue, we propose a new PAEKS security model that captures both (outside) chosen multi-ciphertext attacks and (inside) keyword guessing attacks. Then, we give a concrete PAEKS scheme and prove its security in the new PAEKS security model. We also propose a method to simplify data sender’s key management using identity-based key exchange protocol. Finally, we provide implementation results of our schemes to show the comparable efficiency of our schemes with previous PEKS/PAEKS schemes.

The constrained shortest distance (CSD) query is used to determine the shortest distance between two vertices of a graph while ensuring that the total cost remains lower than a given threshold. The virtually unlimited storage and processing capabilities of cloud computing have enabled the graph owners to outsource their graph data to cloud servers. However, it may introduce privacy challenges that are difficult to address. In recent years, some relevant schemes that support the shortest distance query on the encrypted graph have been proposed. Unfortunately, some of them have unacceptable query accuracy, and some of them leak sensitive information that jeopardizes the graph privacy. In this work, we propose Privacy-preserving G raph encryption for Accurate constrained Shortest distance queries, called PGAS. This solution is capable of providing accurate CSD queries and ensures the privacy of the graph data. Besides, we also propose a secure integer comparison protocol and a secure minimum value protocol that realize two kinds of operations on encrypted integers. We provide theoretical security analysis to prove that PGAS achieves CQA-2 Security with less privacy leakage . In addition, the performance analysis and experimental evaluation based on real-world dataset show that PGAS achieves 100% accuracy with acceptable efficiency.

In the image outsourcing system, image privacy is still an increasing concern since the cloud service provider and image owners are not in the same trusted domain. The most straightforward method for guaranteeing image privacy is to leverage cryptographic tools, but traditional cryptographic tools make feature extraction algorithms useless. To this end, we propose a privacy-preserving feature extraction scheme for Legendre circularly orthogonal moment, which is a novel global feature descriptor and can be used for image analysis. We first develop a novel feature descriptor, which is one of the circularly orthogonal moments and termed as Legendre Circularly Orthogonal Moment (LCOM). Then, we present a mathematical framework for implementing Privacy-preserving Legendre Circularly Orthogonal Moment (PLCOM) by combining LCOM and somewhat homomorphic encryption, and implement the image reconstruction in the encrypted domain based on PLCOM. Besides, the detailed theoretical analysis of message space and expanding factor generated by the quantitative technology shows that LCOM and image reconstruction in the plaintext domain can be realized with the aid of PLCOM. Finally, experimental results verify that the PLCOM’s performance in terms of image reconstruction capability and image recognition accuracy is acceptable.

Training data distributed across multiple different institutions is ubiquitous in disease prediction applications. Data collection may involve multiple data sources who are willing to contribute their datasets to train a more precise classifier with a larger training set. Nevertheless, integrating multiple-source datasets will leak sensitive information to untrusted data sources. Hence, it is imperative to protect multiple-source data privacy during the predictor construction process. Besides, since disease diagnosis is strongly associated with health and life, it is vital to guarantee prediction accuracy. In this paper, we propose a privacy-preserving and high-accurate outsourced disease predictor on random forest, called PHPR . PHPR system can perform secure training with medical information which belongs to different data owners, and make accurate prediction. Besides, the original data and computed results in the rational field can be securely processed and stored in cloud without privacy leakage . Specifically, we first design privacy-preserving computation protocols over rational numbers to guarantee computation accuracy and handle outsourced operations on-the-fly. Then, we demonstrate that PHPR system achieves secure disease predictor. Finally, the experimental results using real-world datasets demonstrate that PHPR system not only provides secure disease predictor over ciphertexts, but also maintains the prediction accuracy as the original classifier.

The development of both flexible solid-state Zn-air and Al-air batteries are challenged by the efficient and stable air cathodes with high catalytic activities in both the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER). In this work, we report the rational design of hollow N-doped carbon nanotube arrays embedded with confined Co nanoparticles (HCA-Co) through a facile solution-reaction and annealing process. Due to the unique integration of hollow carbon nanoarray with tiny cobalt nanoparticles, the obtained flexible HCA-Co electrode shows promising catalytic properties toward both ORR and OER that achieves a current density of 10 mA cm−2 at small overpotential of 290 mV in OER, and demonstrates an onset potential of 0.92 V in ORR. The HCA-Co can be applied as a binder-free air-cathode for flexible all-solid-state zinc-air batteries, which presents a relatively high open circuit potential (1.40 V) with better cycling stability than Pt/C based battery. The HCA-Co is also utilised as cathode for solid-state Al battery, which shows a high open circuit potential (1.966 V) with better mechanical flexibility than that of Pt/C-based battery. Such flexible electrode with excellent bifunctional catalytic properties hold great promise for the application in flexible electronics.

Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in IoT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user secret keys due to different reasons. In this paper, we present a practical attribute-based access control system for IoT cloud by introducing an efficient revocable attribute-based encryption scheme that permits the data owner to efficiently manage the credentials of data users. Our proposed system can efficiently deal with both secret key revocation for corrupted users and accidental decryption key exposure for honest users. We analyze the security of our scheme with formal proofs, and demonstrate the high performance of the proposed system via experiments.

Benefiting from the fast development of human-carried mobile devices , crowd sensing has become an emerging paradigm to sense and collect data. However, reliability of sensory data provided by participating users is still a major concern. To address this reliability challenge, truth discovery is an effective technology to improve data accuracy, and has garnered significant attention. Nevertheless, many of state of art works in truth discovery, either failed to address the protection of participants’ privacy or incurred tremendous overhead on the user side. In this paper, we first propose a privacy-preserving truth discovery scheme, named PPTDS-I, which is implemented on two non-colluding cloud platforms. By capitalizing on properties of modular arithmetic, this scheme is able to protect both users’ sensory data and reliability information, and simultaneously achieve high efficiency and fault-tolerance. Additionally, for the scenarios with resource constrained devices, an efficient truth discovery scheme, named PPTDS-II, is presented. It can not only protect users’ sensory data, but also avoids user participation in the iterative truth discovery procedure. Detailed security analysis shows that the proposed schemes are secure under a comprehensive threat model. Furthermore, extensive experimental analysis has been conducted, which proves the efficiency of the proposed schemes.

In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients’ healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient’s sensitive medical data, but also hampers the first-aid treatment when the patient’s life is threatened because the on-site first-aid personnel are not permitted to get patient’s historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patient’s historical medical data can be recovered using a password-based break-glass access mechanism. To save the storage overhead in the big data storage system, a secure deduplication method is designed to eliminate the duplicate medical files with identical data, which may be encrypted with different access policies. A highlight of this smart secure deduplication method is that the remaining medical file after the deduplication can be accessed by all the data users authorized by the different original access policies. This smart healthcare big data storage system is formally proved secure, and extensive comparison and simulations demonstrate its efficiency.

Herein, we have developed a facile process of synthesizing N and O surface-terminated 2D molybdenum carbide nanomeshes (Mo2CTx NMs) and assembling them into 3D microflowers (Mo2CTx MFs) by one-step pyrolysis of Mo/Zn bimetallic imidazolate frameworks. When used as an oxygen evolution reaction (OER) catalyst, the Mo2CTx NMs thus derived exhibit outstanding catalytic activity with an overpotential of 180 mV at the current density of 10 mA cm−2. This enables Mo2CTx NMs to become one of the best OER electrocatalysts ever reported, with the desired stability in alkaline environment which is a major challenge for most of the non-oxide/hydroxide based electrocatalyts. Additionally, the Mo2CTx MFs can catalyze the hydrogen evolution reaction (HER) and act as bifunctional electrocatalysts for overall water splitting which can attain a current density of 10 mA cm−2 at 1.7 V. Mo LIII-edge X-ray near-edge absorption studies combined with theoretical calculations imply that surface-terminated oxygen is crucial in activating the outstanding OER performance, whereas the top Mo atomic sites on the surface contribute to excellent HER performance.

Two-dimensional (2D) heterojunction held together by van der Waals or covalent interactions give high flexibility in modifying their electrocatalytic activity and ion storage performance, since the surface work function and electronic states are dependent on the topmost layer, also the overall heterostructure. The MoSe2/MoS2 heterostructure based on 2D MoSe2 thin-flake and MoS2 nanosheet was designed through heterojunction engineering and prepared by epitaxial growth process. The abundant interfaces in the MoSe2/MoS2 heterostructure not only enable more exposed active sites for electrochemical reaction, but also facilitate the charge transport due to the open porous space within the interlaced nanosheet arrays, arising from with the synergistic effect by the combination of MoS2 and MoSe2. As expected, the MoSe2/MoS2 heterostructure exhibits excellent hydrogen evolution property with a small Tafel slope of 61 mV dec−1, lower overpotential of 162 mV at 10 mA cm−2, and long-term stability. It also delivers a much boosted supercapacitance performance with a high specific capacitance of 1229.6F g−1 at 1 A g−1 and 92.8% capacitance retention after 2000 cycles. The asymmetric supercapacitor made of the MoSe2/MoS2//nitrogen-doped carbon shows a stable potential window of 1.8 V.

Copper and Fe are redox-sensitive metals, and their isotopic compositions may potentially record changes of oxidation conditions in high-temperature magmatic Ni–Cu mineralization systems. High-precision Cu and Fe isotope data for sulfides (chalcopyrite) and whole-rock samples of the Tulaergen magmatic Ni–Cu system (NW China) were analyzed to evaluate redox-induced fractionation during segregation of sulfide melt from silicate melt and internal fractionation within segregated sulfide melt. Sulfide mineralization includes disseminated and massive types, with massive sulfides being further divided into Cu- and Fe-rich ores. Numerical modeling using mass-balance and Rayleigh equations indicate that disseminated sulfide mineralization was generated from a common parental magma, and massive sulfides were formed by monosulfide solid-solution (MSS)–residual sulfide liquid fractionation. During segregation of sulfide melt from silicate melt, crystallization of olivine and pyroxenes with sulfide segregation, in an Fe2+-dominated phase, led to the incorporation of lighter Fe isotopes in these minerals. The residual silicate melt became progressively more oxidized, with δ56Fewhole-rock values increasing as melts evolved. The disseminated chalcopyrite formed in early stages has lighter Cu and heavier Fe isotopic compositions than the disseminated sulfides formed in later stages due to charge-balance effects. Minor accumulated Ni–Cu sulfide melt was fractionated into an Fe-rich MSS cumulate and a Cu-rich sulfide liquid. MSS crystallization caused the oxygen fugacity of the evolved sulfide liquid to increase, which was accompanied by increasing δ65Cu and decreasing δ56Fe values in chalcopyrite. Iron isotopic compositions of the whole system were shifted towards heavier values from MSS cumulate to the evolved sulfide melt. Numerical modeling using the Rayleigh equation indicates that the fractionation factors α65Curesidual sulfide melt–MSS and α56Feresidual sulfide melt–MSS are ∼1.0011 and ∼1.0005, respectively, during internal fractionation within segregated sulfide melt. This study demonstrates that redox reactions play a key role in Cu and Fe isotope fractionation in high-temperature magmatic Ni–Cu mineralization systems. Furthermore, Cu and Fe isotopes can be used to trace concealed orebodies. Elevated δ65Cu and δ56Fewhole-rock values may indicate Cu-rich mineralization potential, while light Cu and Fe isotopic compositions imply favorable hosts for disseminated and Fe-rich orebodies in mafic–ultramafic intrusions.

The rapid increase in user base and technological penetration has enabled the use of a wide range of devices and applications. The services are rendered to these devices from single-server or highly distributed server environments, irrespective of their location. As the information exchanged between servers and clients is private, numerous forms of attacks can be launched to compromise it. To ensure the security, privacy, and availability of the services, different authentication schemes have been proposed for both single-server and multi-server environments. The primary performance objective of such schemes is to prevent most (if not all) attacks, with minimal computational costs at the server and user ends. To address this challenge, this paper presents a secure user authentication scheme with anonymity (SUAA) for single-server and multi-server environments. It works on 3-factor authentication, involving passwords, smart cards, and biometric data. We use symmetric and asymmetric encryption for single-server and multi-server architectures respectively, to reduce the computational costs. Through a comprehensive security analysis, we show that the proposed scheme is reliable through mutual authentication, and is resilient to attacks addressed by state of the art solutions. Time cost analysis also shows less time required to complete the authentication process.

Efficient and stable air cathodes which catalyze both the oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) are highly desirable but challenging for flexible and rechargeable Zn-air batteries. Here we synthesize unique hybrid cobalt/cobalt nitride (Co/CoNx) nanoparticles well-integrated with nitrogen-doped carbon (NC) nanoarrays through a facile fabrication using a two-dimensional metal–organic framework (MOF) precursor. Such NC-Co/CoNx nanoarrays show promising bifunctional catalytic properties toward both ORR and OER, and can be directly used as an active and durable air cathode for flexible sandwich-like layered Zn-air batteries. In addition, a coaxial fiber-shaped solid-state Zn-air battery is assembled using a carbon microfiber covered with the NC-Co/CoNx nanoarrays as the cathode, a Zn microfiber as the anode and a gel electrolyte. Such fiber-shaped Zn-air battery exhibits much enhanced volumetric power density coupled with good flexibility, showing promising application for flexible energy storage devices.

With the popularity of mobile phones and the ubiquity of wireless transmission technologies, spatial crowdsourcing (SC) has emerged as a novel approach to outsource location-based tasks to a set of workers who physically move to the designated locations to perform the tasks. To achieve the accurate task matching, both requesters and workers need to expose their locations or queries to the SC-Server, which raises security concerns. Although many protection measures have been proposed, there are some drawbacks in one-side protection, dual-server setting and user scalability when they are applied to the practical crowdsourcing environment. In this paper, we design a general framework for spatial task matching in a single-server setting to simultaneously protect the privacy for both tasks and workers. Combining multi-user searchable encryption with segment tree, we propose two different schemes to achieve the spatial task matching over the encrypted data. Efficient user enrollment and revocation are also supported. Extensive experiments validate the feasibility of our schemes.

Partial isomorphic substitution of Zinc in a two-dimensional Cobalt-based (Co-based) bimetallic Metal Organic Frameworks (MOFs) has been successfully synthesised on carbon cloth. Specifically, the Zn content corresponding to the Co to Zn ratio of 2:1 has been incorporated into the Co-based framework during the growth process. Cobalt-based bimetallic oxides ZnCo2O4 are then derived: one from the Bimetal Organic Frameworks via oxidation in air, and the other by first annealing in N2 for stabilisation before oxidation. Systematic characterisation results, including those using XRD, SEM, EDS, TEM and XPS, support the effective incorporation of Zn into the Metal Organic Frameworks. The nanocrystalline bimetallic oxides derived from MOFs exhibit flake-like morphology, the size and thickness of which are affected by Zn addition. Electrochemical performance of the MOF-derived nanocrystalline bimetallic oxides assembled on carbon cloth was examined in a KOH electrolyte (3 M) as the electrolyte at room temperature. The bimetallic oxides show an improvement in specific capacity by more than 300% over that of MOF-derived Co3O4 when used as an electrode material in supercapacitors, together with much improved stability. The capacity loss is reduced from 30.0% to less than 5.2% after 2000 charge–discharge cycles. This work illustrated the potential of bimetallic oxide structures derived from MOFs in delivering high supercapacitor performance.

Searchable Encryption (SE) enables a user to search over encrypted data, such as data stored in a remote cloud server. Existing certificate-, identity-, and attribute-based SE schemes suffer from certificate management or key escrow limitations. Furthermore, the semi-honest-but-curious cloud may conduct partial search operations and return a fraction of the search results (i.e., incomplete results) in order to reduce costs. In this paper, we present a secure cryptographic primitive, Verifiable Multiple Keywords Search (VMKS) over ciphertexts, which leverages the Identity-Based Encryption (IBE) and certificateless signature techniques. The VMKS scheme allows the user to verify the correctness of search results and avoids both certificate management or key escrow limitations. We then demonstrate the security of proposed VMKS scheme (i.e., the scheme achieves both ciphertext indistinguishability and signature unforgeability). We also use a real-world dataset to evaluate its feasibility and efficiency.

As an attractive business model of cloud computing, outsourcing services usually involve online payment and security issues. The mutual distrust between users and outsourcing service providers may severely impede the wide adoption of cloud computing. Nevertheless, most existing payment solutions only consider a specific type of outsourcing service and rely on a trusted third-party to realize fairness. In this paper, in order to realize secure and fair payment of outsourcing services in general without relying on any third-party, trusted or not, we introduce BCPay, a blockchain based fair payment framework for outsourcing services in cloud computing. We first present the system architecture, specifications and adversary model of BCPay, then describe in detail its design. Our security analysis indicates that BCPay achieves Soundness and what we call Robust Fairness, where the fairness is resilient to eavesdropping and malleability attacks. Furthermore, our performance evaluation shows that BCPay is very efficient in terms of the number of transactions and computation cost. As illustrative applications of BCPay, we further construct a blockchain-based provable data possession scheme in cloud computing and a blockchain-based outsourcing computation protocol in fog computing.

In this paper, we propose a privacy-preserving e-health system, which is a fusion of Internet-of-things (IoT), big data and cloud storage. The medical IoT network monitors patient’s physiological data, which are aggregated to electronic health record (EHR). The medical big data that contains a large amount of EHRs are outsourced to cloud platform. In the proposed system, the patient distributes an IoT group key to the medical nodes in an authenticated way without interaction round. The IoT messages are encrypted using the IoT group key and transmitted to the patient, which can be batch authenticated by the patient. The encrypted EHRs are shared among patient and different data users in a fine-grained access control manner. A novel keyword match based policy update mechanism is designed to enable flexible access policy updating without privacy leakage. Extensive comparison and simulation results demonstrate that the algorithms in the proposed system are efficient. Comprehensive analysis is provided to prove its security.

Fatty acid synthase (FASN) catalyzes the synthesis of palmitate, which is required for formation of complex fatty acids and phospholipids that are involved in energy production, membrane remodeling and modification of host and viral proteins. Presently, the roles of cellular fatty acid synthesis pathway in Autographa californica multiple nucleopolyhedrovirus (AcMNPV) infection is not clear. In this study, we found that the transcripts level of fasn was significantly up-regulated at the early stage of AcMNPV infection. Treatment of AcMNPV-infected Spodoptera frugiperda Sf9 cells with C75, a specific inhibitor of FASN, did not affect the internalization of budded virions into cells, but dramatically reduced the infectious AcMNPV production. Further analysis revealed that the presence of C75 significantly decreased the expression level for two reporter genes, beta-galactosidase and beta-glucuronidase, that were separately directed by the early and late promoter of AcMNPV. Similarly, Western blot analysis showed that, in C75-treated cells, the expression of viral gp64 was delayed and decreased. Additionally, treatment with C75 also resulted in a significant reduction in the accumulation of viral genomic DNA. Together, these results demonstrate that the fatty acid synthesis pathway is required for efficient replication of AcMNPV, but it might not be necessary for AcMNPV entry into insect cells.

Electronic health (e-health) social system provides an effective way for the patients to share their treatment experience, exchange medical information and build a supportive relationship. In this paper, we propose a novel symptom-matching based group key management scheme for the e-health social system supporting dynamic group membership change. The patients in this system are diagnosed and treated by different medical institutions. This proposed schemes allows a group of patients from different healthcare domains (cross-domain) to securely establish a group session key to protect the group disease discussion. The scheme supports patient anonymity and traceability since the identities of the patients are hidden in an anonym and their medical institution is able to recover the real identity. The group agreement protocol ensures that only the authenticated patient with the same symptom could derive the group session key. The privacy of patient’s symptom is also protected since the patient cannot know the other patients’ symptoms if they do not have the same symptom. The security of this scheme is proved and the performance is evaluated theoretically and experimentally. The simulation and comparison indicate that our scheme has good performance and suitable for the mobile e-health social system.

Earth-abundant, efficient and durable electrocatalysts for water splitting are vitally important for a sustainable energy system. Here we report the rational design of hollow Mo-doped CoP (Mo-CoP) nanoarrays, which simultaneously combine electronic structure modification through doping with a high density of reaction sites through nanostructuring. With this strategy the Mo-CoP arrays give significantly improved hydrogen evolution reaction (HER) performance, and also, when in situ transformed into Mo-doped CoOOH (Mo-CoOOH) arrays, excellent activity towards the oxygen evolution reaction (OER) is obtained. The origin of the improvement is determined by atomic-resolution imaging combined with density functional theory (DFT). An electrolyzer using Mo-CoP and Mo-CoOOH can be powered by a single AA battery (~1.5 V), and maintains a stable water-splitting current for 20 h, superior to most reported electrocatalysts in alkaline media, offering great promise for practical applications.

Data security and privacy concerns in cloud storage services compel data owners to encrypt their sensitive data before outsourcing. Standard encryption systems, however, hinder users from issuing search queries on encrypted data. Though various systems for search over encrypted data have been proposed in the literature, existing systems use different encrypted index structures to conduct search on different search query patterns and hence are not compatible with each other. In this paper, we propose a query over encrypted data system which supports expressive search query patterns, such as single/conjunctive keyword query, range query, boolean query and mixed boolean query, all using a single encrypted index structure. To the best of our knowledge, the proposed system enables the most expressive query pattern search among all the existing solutions. In addition, the system allows data users to simultaneously query over encrypted documents from multiple data owners using one query trapdoor and supports flexible user authorization and revocation. We show that our system is secure and resists keyword guessing attack. We also conduct extensive experiments and demonstrate that the system is more efficient than other public key searchable encryption systems.

In this paper, we propose a framework for hybrid privacy-preserving clinical decision support system in fog–cloud computing, called HPCS. In HPCS, a fog server uses a lightweight data mining method to securely monitor patients’ health condition in real-time. The newly detected abnormal symptoms can be further sent to the cloud server for high-accuracy prediction in a privacy-preserving way. Specifically, for the fog servers, we design a new secure outsourced inner-product protocol for achieving secure lightweight single-layer neural network. Also, a privacy-preserving piecewise polynomial calculation protocol allows cloud server to securely perform any activation functions in multiple-layer neural network. Moreover, to solve the computation overflow problem, a new protocol called privacy-preserving fraction approximation protocol is designed. We then prove that the HPCS achieves the goal of patient health status monitoring without privacy leakage to unauthorized parties by balancing real-time and high-accurate prediction using simulations.