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Semi-quantum cryptography communication stipulates that the quantum user has complete quantum capabilities, and the classical user has limited quantum capabilities, only being able to perform the following operations: (1) measuring and preparing qubits with a Z basis and (2) returning qubits without any processing. Secret sharing requires participants to work together to obtain complete secret information, which ensures the security of the secret information. In the semi-quantum secret sharing (SQSS) protocol, the quantum user Alice divides the secret information into two parts and gives them to two classical participants. Only when they cooperate can they obtain Alice's original secret information. The quantum states with multiple degrees of freedom (DoFs) are defined as hyper-entangled states. Based on the hyper-entangled single-photon states, an efficient SQSS protocol is proposed. The security analysis proves that the protocol can effectively resist well-known attacks. Compared with the existing protocols, this protocol uses hyper-entangled states to expand the channel capacity. The transmission efficiency is 100% higher than that of single-degree-of-freedom (DoF) single-photon states, providing an innovative scheme for the design of the SQSS protocol in quantum communication networks. This research also provides a theoretical basis for the practical application of semi-quantum cryptography communication. [ABSTRACT FROM AUTHOR]

In recent years, network coding has received extensive attention and has been applied to various computer network systems, since it has been mathematically proven to enhance the network robustness and maximize the network throughput. However, it is well-known that network coding is extremely vulnerable to pollution attacks. Certificateless network coding scheme (CLNS) is a recently proposed mechanism to defend against pollution attacks for network coding, which avoids tedious management of certificates and key-escrow attack. Until now, only a few constructions were presented, and more ones should be given so as to enrich this field. In this paper, for the first time, we study the general construction of CLNS from certificateless public auditing protocol (CL-PAP), although the two areas seem to be quite different in their nature and are studied independently. Since there are many candidates of CL-PAPs, we can naturally obtain abundant constructions of CLNSs according to our systematic way. In addition, in order to show the power of the general construction, we also present a concrete implementation given a specific CL-PAP. The performance analysis and experimental results show that the implemented CLNS is competitive in the existing network coding schemes. [ABSTRACT FROM AUTHOR]

The optimization of traditional FTL(Flash Translation Layer) algorithm is mainly aimed at the average response time of flash memory read and write operation on a flash chip, because of the Out-of-place update, traditional FTL algorithm writes data in a new free page each time, and a new block is assigned when a block is full. Therefore, when the flash memory is almost full, a written request will lead to a garbage collection, in which there will be many write copies, which will lead to a significant decrease in response time. In this paper, we optimize the traditional FTL algorithm and propose an FTL algorithm that guarantees real-time performance by shortening the worst response time of the request as a measure of the guaranteed flash Qos (Quality of Service) under the condition that the address mapping is provided the upper layer. The algorithm adopts the idea of space-for-time and spreads the written copy of flash memory to each write request so that the worst response time of the write request is significantly reduced. The algorithm also wears balancing considerations to the algorithm. After experimental verification, the proposed algorithm has advantages over existing FTL algorithms in terms of real-time performance and has a performance improvement of more than 11% in terms of average response time and wear balance. By the way, we add the wear-level module into an algorithm. According to improving the algorithm, the worst response time has shorten 47.6%. [ABSTRACT FROM AUTHOR]

Deduplication is a popular data reduction technology in storage systems which has significant advantages, such as finding and eliminating duplicate data, reducing data storage capacity required, increasing resource utilization, and saving storage costs. The file features are a key factor that is used to calculate the similarity between files, but the similarity calculated by the single feature has some limitations especially for the similar files. The storage node feature reflects the load condition of the node, which is the key factor to be considered in the data routing. This paper introduces a multifeature data routing strategy (DRMF). The routing strategy is made based on the features of the cluster, including routing communication, file similarity calculation, and the determination of the target node. The mutual information exchange is achieved by routing communication, routing servers, and storage nodes. The storage node calculates the similarity between the files stored, and then the file is routed according to the information provided by the routing server. The routing server determines the target node of the route according to the similar results and the node load features. The system prototype is designed and implemented; also, we develop a system to process the feature of cluster and determine the specific parameters of various features of experiments. In the end, we simulate the multifeature data routing and single-feature data routing, respectively, and compare the deduplication rate and data slope between the two strategies. The experimental results show that the proposed data routing strategy using multiple features can improve the deduplication rate of the cluster and maintain a lower data skew rate compared with the single-feature-based routing strategy MCS; DRMF can improve the deduplication rate of the cluster and maintain a lower data skew rate. [ABSTRACT FROM AUTHOR]

In PKC 2000, Pointcheval presented a generic technique to make a highly secure cryptosystem from any partially trapdoor one-way function in the random oracle model. More precisely, any suitable problem providing a one-way cryptosystem can be efficiently derived into a chosen-ciphertext attack (CCA) secure public key encryption (PKE) scheme. In fact, the overhead only consists of two hashing and a XOR. In this paper, we consider the key-dependent message (KDM) security of the Pointcheval's transformation. Unfortunately, we do not know how to directly prove its KDM-CCA security because there are some details in the proof that we can not bypass. However, a slight modification of the original transformation (we call twisted Pointcheval's scheme) makes it possible to obtain the KDM-CCA security. As a result, we prove that the twisted Pointcheval's scheme achieves the KDM-CCA security without introducing any new assumption. That is, we can construct a KDM-CCA secure PKE scheme from partial trapdoor one-way injective family in the random oracle model. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing technology and the broad application of the Internet, more and more user data are stored in the cloud storage system online. Data copy technology is an effective technical means to manage various data in cloud storage systems, which has the advantages of improving data access speed and enhancing data availability in cloud storage systems. Since a data copy management strategy has an important impact on the performance of cloud storage systems, how to design a data copy management strategy is of great significance to achieving high-quality cloud storage management. Firstly, this paper analyzes the HDFS random copy placement strategy and its shortcomings. Secondly, proposes a copy placement strategy based on evaluation value and load balancing. Thirdly, gives the evaluation function of rack and node by considering multiple evaluation indicators such as node disk space load capacity, node memory utilization, node CPU utilization, rack load rate and network distance between racks. Fourthly, finds suitable nodes through computer shelf evaluation values and node evaluation values when placing replicas. Finally, through simulation experiments, the experimental results of the HDFS random replica placement strategy and the evaluation results and load balancing-based replica placement strategy are compared and analyzed. [ABSTRACT FROM AUTHOR]

As a fast-rising storage model in recent years, cloud storage has adopted a "pay-as-you-go" approach to provide users with highly reliable, highly available, low-cost, and secure storage services that have received widespread attention and use within enterprises and individuals. Data replica management technology, as an essential part of cloud storage systems, has irreplaceable advantages in improving cluster fault tolerance and availability, so it has become the focus of many experts and scholars. It replicates multiple data blocks and places them in various nodes in the cluster; this makes the data more secure and reliable and improves the access rate while ensuring system load balance. Data replica technology runs through the process from replica creation to consistency maintenance. Each part of it has an essential impact on the performance of the cloud storage system. This article focuses on the dynamic decision of the number of data replicas in the cloud storage system. Considering the shortcomings of the static replica strategy, a dynamic decision strategy for the number of replicas based on the popularity of the data is proposed. By using the gray prediction model GM (1, 1) to predict the future data access frequency and using the Markov model to modify the prediction result, the data can be divided into hot data and non-hot data according to the predicted value, thereby determining the data replica number. Finally, through simulation experiments, the experimental results of the static replica strategy and the data hot-based replica number dynamic decision strategy are compared and analyzed. [ABSTRACT FROM AUTHOR]

Cloud computing platform is a data-centric architecture, which utilize virtualization resource scheduling technology to integrate and distribute the processing power of distributed server cluster and other network devices distributed over the network, and uses service-oriented architecture to provide users with highly reliable, highly available, and efficient application data services. Although the cloud platform application based on various requirements has become a research hotspot in cloud computing technology, in some high Real-time requirements application scenarios, the research on how to guarantee the real time performance of cloud platform is few. Compared to traditional resource virtualization such as CPU and memory, Docker only provides a small portion of network resource virtualization. The four network modes provided by Docker officially have a single function, which makes it very difficult to apply to cloud platform scenarios with complex network bandwidth requirements and high real-time requirements. Based on Docker's traditional NAT communication method, this paper proposes a network virtualization solution based on SDN and Docker containers. Through comparison experiments, it is proved that the scheme can guarantee the Real-time performance of the container cloud platform. [ABSTRACT FROM AUTHOR]

Natural gas load forecasting provides decision‐making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas load forecasting, this study predicts the natural gas load volatility. As the natural gas load volatility has the time‐series features, along with long‐term memory, volatility aggregation, asymmetry, and nonnormality, this study proposes a natural gas load volatility prediction model by combining generalized autoregressive conditional heteroscedasticity (GARCH) family models, XGBoost algorithm, and long short‐term memory (LSTM) network. The model first takes the GARCH family models parameters of sliding estimation and meteorological factors as the influencing factors of volatility, and then it screens these influencing factors through the extreme gradient boosting (XGBoost) algorithm. Finally, the selected important features are input into the LSTM network to predict the volatility, and the 90% confidence interval of the volatility is calculated. Compared with a variety of single and combined models, the model proposed in this study has an average reduction of 45.404% in the evaluation index of mean squared error. The experimental results show that the model proposed in this study has a good performance and accuracy in predicting the volatility of natural gas load. [ABSTRACT FROM AUTHOR]

Towards the target of image retouching tasks, the three-dimension LookUp Table (3D LUT) is a discrete 3D lattice that represents the color mapping to correct the inconsistent color of images produced from varying devices and environments. We design a network named Dual Branch LUT-aware Network (DualBLN), which innovatively introduces the information representing the pixel value mapping in 3D LUT into the real-time retouching procedure, learning the adaptive weights and the multiple 3D LUTs with strong representation capability. Specifically, we elaborate two branches to conduct feature extraction of the raw image and 3D LUTs, which consider the detail of the dual inputs, then generate the precise weights to fuse LUTs for graceful retouching. To integrate the information of the image and the 3D LUTs meticulously, we adopt bilinear pooling to alleviate the conflict between both features when fusing them from two branches of the network, which avoids the feature distortion caused by concatenation or summation directly. In addition, to meet the needs of portrait photo retouching (PPR) for portrait area priority (HRP), we propose Human Region Mask Generator (HRMG) module to solve the shortcomings of the current PPR task that requires the manual marking of portrait region, which achieve the PPR task more conveniently and efficiently. Extensive experiments verify the effectiveness of our method, e.g., the PSNR metric receives over 0.6 gains on high-resolution and 1.4 gains on the human-centered region at most compared with prior state-of-art works. • We design a new network to use look-up table carefully for image retouching. • We use bilinear pooling for feature fusion to improve robustness of the model. • We propose a human region segmentation module by fusing multi-scale feature. • Vast experiments on many datasets prove the efficacy of our proposed method. [ABSTRACT FROM AUTHOR]

Accurate forecasting of natural gas consumption (NGC) plays an important role in energy supply, energy trading, economic effects and environmental sustainability. NGC forecasts can be used to adjust production and supply plans to improve gas efficiency and reduce carbon emissions and supply chain waste. This paper reviews the research progress on NGC in the past decade, analyzes the typical characteristics of different forecasting strategies, and highlights 163 studies in terms of the technical aspects of feature processing methods, data decomposition methods, forecasting models and optimization algorithms. It also systematically elaborates the application of statistical models, machine learning models, grey models, logistic regression and their combinations in predictive models. Bibliometric methods are also utilized to dissect research hotspots and summarize cutting-edge trends in the field. It is worth mentioning that in the terms of hybrid model structures, the application and performance of various model structures are described and evaluated. In this paper, the future development is discussed from spatiotemporal characteristics, studying reasonable data decomposition layers and fusion models, considering potential data privacy issues, and developing artificial intelligence-supporting models and interpretable frameworks. This paper is expected to provide a multi-technology reference for natural gas forecasting and help researchers to select and develop more accurate forecasting techniques and strategies. • Bibliometric analysis of natural gas consumption forecasting. • Typical features of different forecasting strategies are reviewed. • Characteristics and applications of forecasting models and assisted forecasting techniques are outlined. • The application and performance of various hybrid model structures are described and evaluated. • Existing problems and future research directions in natural gas consumption forecasting are discussed. [ABSTRACT FROM AUTHOR]

As user-generated data diversifies, an increasing number of big data tasks now encompass unstructured data forms, including audio, imagery, and video content. The processing of these data often requires the support of GPU devices in the cluster. However, most of the existing cluster resource management frameworks lack effective decoupling of CPU resources and GPU resources, and the management granularity of GPU resources is too coarse, resulting in poor GPU sharing and low resource utilization. Given, for the inadequacy of the existing cluster resource management framework that cannot support batch stream integration and CPU-GPU resource scheduling, we design and implement a Hybrid Heterogeneous Resource Management (H-HRM) for CPU-GPU heterogeneous clusters. The resource queue binding mechanism on computing nodes provides flexible binding of CPU resources to GPU resources, which solves the problem that CPU resources and GPU resources in the cluster are difficult to decouple. According to the different uses of CPU or GPU as the main resource, a Hybrid Domain Resource Fairness (HDRF) model is proposed to realize the reasonable allocation of CPU resources and GPU resources. Through the queue stacking technology and the mechanism that multiple executors can run simultaneously on the queue, fine-grained sharing of GPU resources is realized and the utilization rate of GPU resources is improved. Finally, this paper realizes the docking of H-HRM and Spark programming framework and conducts real load tests, and after comparing with the performance of Mesos, it is proved that H-HRM can handle the mixed scenarios of batch jobs and stream processing jobs in the cluster, and the HDRF algorithm also greatly improves the utilization of the GPU. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the I/O cache of the server layer device of the network block device in the embedded background. It proposes a method focusing on different read/write loading features, configuring the cache capacity ratio utilized for isolated processing reading data and writing data, and an algorithm for automatically analyzing sequential addressing writing and random addressing writing data streams. The effectiveness of the algorithm for automatically analyzing sequential addressing writing and random addressing writing data streams is verified by experiments. The experimental results show that according to the different interleaving conditions of the data stream, the proportion of the data stream can be written in sequential addressing, which can be different in all input data streams. After separating sequential data streams interleaved by three inputs, the percentage of data streams written in sequential addressing is significantly increased by 50%. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]

Wind energy is a renewable energy source with great development potential, and a reliable and accurate prediction of wind speed is the basis for the effective utilization of wind energy. Aiming at hyperparameter optimization in a combined forecasting method, a wind speed prediction model based on the long short-term memory (LSTM) neural network optimized by the firework algorithm (FWA) is proposed. Focusing on the real-time sudden change and dependence of wind speed data, a wind speed prediction model based on LSTM is established, and FWA is used to optimize the hyperparameters of the model so that the model can set parameters adaptively. Then, the optimized model is compared with the wind speed prediction based on other deep neural architectures and regression models in experiments, and the results show that the wind speed model based on FWA-improved LSTM reduces the prediction error when compared with other wind speed prediction-based regression methods and obtains higher prediction accuracy than other deep neural architectures. [ABSTRACT FROM AUTHOR]

Internet of Medical Things (IoMT) technology is widely used in intelligent medical treatment; however, massive mobile data transmission makes the cellular network overburden. The combination of medical Internet of Things technology and cloud storage can greatly improve this problem. Most of the existing medical Internet of Things systems cannot be adapted to the environment with limited resources after considering security and privacy, or after ensuring implementation efficiency, it will lead to reduced system security; that is, the mutual authentication function of the system is easily destroyed. The combination of medical Internet of Things technology and medical cloud storage technology can greatly improve the current smart medical environment. We propose an escrow-free identity-based scheme (EF-IDS) to ensure the function of mutual authentication between system entities, propose a secure lightweight cloud-centric smart medical system based on the medical Internet of Things based on EF-IDS and prove that our system ensures the privacy and security of users' personal health information. It also provides the ability to publicly verify the integrity of the data stored on the medical cloud server. Finally, the performance analysis shows that our proposed system has significant advantages in both communication overhead and computational cost. [ABSTRACT FROM AUTHOR]

The dramatic increase of storage devices in distributed storage cluster system and the inherent characteristics of distributed deployment mode make network resources become one of the bottlenecks of data storage process. By analyzing the functional characteristics of data flow transmission of the components of the storage system, the network topology structure of the storage system is constructed, and the evaluation index of node flow load is put forward based on the degree centrality and the betweenness centrality theory of the network to explore the network topology and real-time flow characteristics. According to the evaluation index of node flow load, a network-aware data layout scheme is proposed. By balancing the flow load of bottleneck link, congestion and transmission delay can be reduced to further shorten the total task execution time and improve the efficiency of data writing. [ABSTRACT FROM AUTHOR]

The identification and classification of faults in chemical processes can provide decision basis for equipment maintenance personnel to ensure the safe operation of the production process. In this paper, we combine long short-term memory neural network (LSTM) with convolutional neural network (CNN) and propose a new fault diagnosis method based on multichannel LSTM-CNN (MCLSTM-CNN). The primary methodology here includes three aspects. In the initial state, the fault data are input into the LSTM to obtain the output of the hidden layer, which stores the relevant temporal and spatial domain information. Due to the diversity of data features, convolutional kernels with different sizes are utilized to form multiple channels to extract the output characteristics of the hidden layer simultaneously. Finally, the fault data are classified by fully connected layers. The Tennessee Eastman (TE) chemical process is used for experimental analysis, and the MCLSTM-CNN model is compared with the LSTM-CNN, LSTM, CNN, RF and KPCA + SVM models. The experimental results show that the MCLSTM-CNN model has higher diagnostic accuracy, and the fault classification results are superior to other models. [ABSTRACT FROM AUTHOR]

Provable data possession (PDP) protocol is a mechanism that guarantees the integrity of user's cloud data, and many efficient protocols have been proposed. Many of them ignore data's privacy against the third-party auditor (TPA) and also suffer from intricate management of certificates, which heavily relies on the public key infrastructure (PKI). In order to overcome the two shortcomings, Li et al. recently proposed an "identity-based" (IB) PDP protocol with the privacy-preserving property (IEEE Syst J, https://doi.org/10.1109/JSYST.2020.2978146). However, we find out that (1) their protocol has great communication overhead, (2) a PKI-based signature scheme is used as a building block, which results in their protocol is not completely identity-based. Hence, in this paper, we try to improve the performance of this protocol. Concretely, by adopting flexible data-splitting and tag-aggregating techniques, we can greatly reduce its communication overhead. A concrete example shows that the total communication overhead can be reduced over 99%. Moreover, by replacing with an identity-based signature, we can twist this protocol into a complete IB-PDP protocol. [ABSTRACT FROM AUTHOR]

To identify the unstable individuals of networks is of great importance for information mining and security management. Exploring a broad range of steady-state dynamical processes including biochemical dynamics, epidemic processes, birth–death processes and regulatory dynamics, we propose a new index from the microscopic perspective to measure the stability of network nodes based on the local correlation matrix. The proposed index describes the stability of each node based on the activity change of the node after its neighbor is disturbed. Simulation and comparison results show our index can identify the most unstable nodes in the network with various dynamical behaviors, which would actually create a richer way and a novel insight of exploring the problem of network controlling and optimization. [ABSTRACT FROM AUTHOR]

• Revealing research hotspot and corresponding characteristics by bibliometric methods. • Discovering underlying laws behind data by constructing scientific knowledge graph. • Summarizing potential research hotspots by keyword co-occurrence cluster graph. • Summarizing cutting-edge trends by keyword co-occurrence cluster graph. • Discussing the main open problems in depth and proposing corresponding solutions. With the advent of the era of big data, the recommendation system has become an effective solution to the problem of information overload. This paper takes the literature data related to the recommendation system theme from 2009 to 2018 and included in the core collection of Web of Science database as the research object, and utilizes bibliometric methods to analyze the theme of recommendation system. To this end, firstly, classify statistics and feature analysis of valid literature data. Secondly, use VOSviewer software to construct various different scientific knowledge graph to discover valuable knowledge. Thirdly, according to keyword co-concurrence graph conclude five main hotspots of current research about recommendation system and discover five main directions that have potential value in research field of recommendation system. Finally, deeply explore five main key issues and propose corresponding solutions. [ABSTRACT FROM AUTHOR]

Nickel is a vital strategic metal resource with commodity and financial attributes simultaneously, whose price fluctuation will affect the decision-making of stakeholders. Therefore, an effective trend forecast of nickel price is of great reference for the risk management of the nickel market's participants; yet, traditional forecast methods are defective in prediction accuracy and applicability. Therefore, a prediction model of nickel metal price is proposed based on improved particle swarm optimization algorithm (PSO) combined with long-short-term memory (LSTM) neural networks, for higher reliability. This article introduces a nonlinear decreasing assignment method and sine function to improve the inertia weight and learning factor of PSO, respectively, and then uses the improved PSO algorithm to optimize the parameters of LSTM. Nickel metal's closing prices in London Metal Exchange are sampled for empirical analysis, and the improved PSO-LSTM model is compared with the conventional LSTM and the integrated moving average autoregressive model (ARIMA). The results show that compared with the standard PSO, the improved PSO has a faster convergence rate and can improve the prediction accuracy of the LSTM model effectively. In addition, compared with the conventional LSTM model and the integrated moving average autoregressive (ARIMA) model, the prediction error of the LSTM model optimized by the improved PSO is reduced by 9% and 13%, respectively, which has high reliability and can provide valuable guidance for relevant managers. [ABSTRACT FROM AUTHOR]

The amount of encoded data replication in an erasure-coded clustered storage system has a great impact on the bandwidth consumption and network latency, mostly during data reconstruction. Aimed at the reasons that lead to the excess data transmission between racks, a rack aware data block placement method is proposed. In order to ensure rack-level fault tolerance and reduce the frequency and amount of the cross-rack data transmission during data reconstruction, the method deploys partial data block concentration to store the data blocks of a file in fewer racks. Theoretical analysis and simulation results show that our proposed strategy greatly reduces the frequency and data volume of the cross-rack transmission during data reconstruction. At the same time, it has better performance than the typical random distribution method in terms of network usage and data reconstruction efficiency. [ABSTRACT FROM AUTHOR]

Understanding the development process of biological neuronal systems is one of the major challenges to explore the formation mechanism of human brain intelligence and biological behaviors, which is of great importance to enlighten the designing of various artificial neural network algorithms as well. Here, with the whole neural connection map of C.elegans , we explore the evolutionary mechanism of its neuronal system based on mean-field theory and network dimension reduction method. Firstly, we use a set of activation equations to capture the neurons' interaction in the networks, and adopt the dimensionality reduction framework to decouple the nematode's multi-dimensional neural network into an one-dimensional system. Then we propose two control index after the system is decoupled, which can be used to describe the structure and stable state of the whole neural network, as well as the development of an organism on the basis of birth time and process length. Our theoretical approach of multidimensional system to the one-dimensional system can help distract our attention from the micro-dynamics of a single neuron to the macro-dynamics of the whole network. Our results reveal some factors that influence the evolution of neural system, explore the evolutionary constraints and contribution of these factors at the level of neural networks function and topology. In conclusion, our analytical framework provides an overview of quantitative understanding of the growth process and the evolutionary constraints in the neural system. • Propose a neural network dimensionality reduction method. • Measure neural importance with birth time and process length. • Describe quantitatively the evolution of nematode neural networks. • Reveal evolutionary constraints of neural network's function and topology. [ABSTRACT FROM AUTHOR]

• In this paper, we revisit the CL-PDP scheme proposed by Wang et al. in 2017 since their protocol is attacked in recent work of Liao et al. We modify their original scheme and hence obtain a twisted one. • We also find out Want et al.'s security model is not proper because they merged the CSP and KGC into one entity. In our new security model, we prove that the twisted HKWWC-protocol is secure. Certificateless provable data possession (CL-PDP) protocol is an important tool to check the integrity of data outsourced to cloud service provider (CSP) since it is not necessary to consider the certificate management and key escrow problems. In 2017, He et al. proposed an efficient CL-PDP protocol (HKWWC-protocol, for short) with an additional good property: Privacy protection from the verifier Appl. Math. Comput., vol. 314, pp. 31-43. However, recently, Liao et al. illustrates that the HKWWC-protocol is completely insecure under their suggested attack (LLON-attack) since CSP can return a forged proof that is able to pass the verification of the verifier, which shows that the HKWWC-protocol will become completely useless. In this paper, we revisit the original HKWWC-protocol and try to rescue it. After our modification, the twisted new protocol can be immune to the LLON-attack. More importantly, we find that the original He et al.'s security model cannot depict the practical scene at all because they viewed CSP and KGC (key generation center) as one entity in their system. Hence, we redefine a new security model and prove the twisted HKWWC-protocol is secure under our new security model. Performance analysis shows that our proposed protocol is still efficient and practical. [ABSTRACT FROM AUTHOR]