摘要 ：

The rapid growth of various vehicular apps such as automotive navigation and in-car entertainment has brought the explosion of vehicular data. Such a growth has given rise to a huge challenge of maintaining the quality of cloud storage services for the whole period of storage in vehicular networks. As a result, poor quality of services easily causes data corruption problems and thereby threats vehicular data integrity. Blockchain, a tamper-proofing technique, is considered a promising approach for mitigating data integrity risks in cloud storage. However, existing blockchain-based schemes for auditing long-term cloud data integrity suffer from poor communication performance in a vehicular network. In this study, a blockchain-based offline auditing scheme for cloud storage in the vehicular network is proposed to improve auditing performance. Inspired by the data structure of blockchain, we design an evidence chain to achieve offline auditing, which allows the cloud to spontaneously generate data integrity evidence without communicating with auditors during the evidence generation phase. Furthermore, we extend our scheme to support public and automatic validation based on the smart contract. We prove the security of the proposed scheme under the random oracle model and further provide the performance evaluation by comparing with the state-of-the-art approaches.

关键词 ：

Metadata Vehicle dynamics Blockchains Security vehicular network Data integrity offline auditing Cloud storage blockchain Cloud computing Vehicular ad hoc networks

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摘要 ：

The encryption and signature schemes based on traditional public key are no longer applicable for devices with limited hardware and software resources in the Internet of Things (IoT), since those schemes consume too much hardware and software. To ensure the security and efficiency of data transmission, we construct a secure and lightweight certificateless hybrid signcryption scheme for Compared with the existing schemes, this scheme only needs one bilinear calculation for one communication, which is computationally efficient for protecting the secure communication between nodes with limited resources in IoT. To meet the security requirements of the complex IoT, based on difficult problem assumption of bilinear pairings, this scheme can simultaneously meet security requirements of the IoT such as confidentiality, unforgability, semipublic verification, forward security known session-specific temporary information security. In addition, we prove the security and correctness of this scheme in Random Oracle Model (ROM). (C) 2021 Elsevier B.V. All rights reserved.

关键词 ：

Internet of Things Random oracle mode Certificateless hybrid signcryption

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摘要 ：

Decentralized storage powered by blockchain is becoming a new trend that allows data owners to outsource their data to remote storage resources offered by various storage providers. Unfortunately, unqualified storage providers easily encounter unpredictable downtime due to security threats, such as malicious attacks or system failures, which is unacceptable in many real-time or data-driven applications. As a result, continuous data integrity should be guaranteed in decentralized storage, which ensures that data is intact and available for the entire storage period. However, this requires frequent checking for long time periods and incurs heavy burdens of both communication and computation, especially in a big data scenario. In this paper, we propose an efficient continuous big data integrity checking approach for decentralized storage. We design a data-time sampling strategy that randomly checks the integrity of multiple files at each time slot with high checking probability. Furthermore, to tackle the fairness problem derived from the sampling strategy, we propose a fair approach by designing an arbitration algorithm with the verifiable random function. Security analysis shows the security of our approach under the random oracle model. Evaluation and experiments demonstrate that our approach is more efficient in the big data scenario compared with the state-of-the-arts.

关键词 ：

Big data Big Data Blockchain Cloud computing Data integrity data integrity checking decentralized storage Metadata sampling Security Servers verifiable random function

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Emerging applications in machine learning have imposed the problem of monotone non-submodular maximization subject to a cardinality constraint. Meanwhile, parallelism is prevalent for large-scale optimization problems in bigdata scenario while adaptive complexity is an important measurement of parallelism since it quantifies the number of sequential rounds by which the multiple independent functions can be evaluated in parallel. For a monotone non-submodular function and a cardinality constraint, this paper devises an adaptive algorithm for maximizing the function value with the cardinality constraint through employing the generic submodularity ratio gamma to connect the monotone set function with submodularity. The algorithm achieves an approximation ratio of 1 - e-(gamma 2) - epsilon and consumes O(log(n/eta)/epsilon(2)) adaptive rounds and O(n log log (k)/epsilon(3)) oracle queries in expectation. Furthermore, when gamma=1, the algorithm achieves an approximation guarantee 1 - 1/e - epsilon, achieving the same ratio as the state-of-art result for the submodular version of the problem.

关键词 ：

Non-submodular optimization Cardinality constraint Parallel algorithm Submodularity ratio

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Function-on-scalar regression is commonly used to model the dynamic behaviour of a set of scalar predictors of interest on the functional response. In this article, we develop a robust variable selection procedure for function-on-scalar regression with a large number of scalar predictors based on exponential squared loss combined with the group smoothly clipped absolute deviation regularization method. The proposed procedure simultaneously selects relevant predictors and provides estimates for the functional coefficients, and achieves robustness and efficiency using tuning parameters selected by a data-driven procedure. Under reasonable conditions, we establish the asymptotic properties of the proposed estimators, including estimation consistency and the oracle property. The finite-sample performance of the proposed method is investigated with simulation studies. The proposed method is also demonstrated with a real diffusion tensor imaging data example.

关键词 ：

variable selection robust estimation oracle property group SCAD Functional data analysis

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Data backup is a pervasive strategy adopted by cloud service providers (CSPs) to avoid potential risks of data loss. As a result, a CSP normally maintains multiple replicas for each piece of data on geographically distributed servers to improve reliability. A key problem of this replication technique is that users will be charged more when more replicas are stored. Therefore, an auditing service is desired to help users verify whether an untrusted CSP stores all their replicas in different geographic locations or not. In this paper, we propose a dynamic multi-replica auditing scheme that has the following features: (1) it can verify both the integrity and geographic locations of a cloud user's data replicas; (2) the proposed scheme can identify different copies with geographic locations, which reduces the complexity in data preprocessing, dynamic operations and backup recovery; (3) by introducing an Indexed Merkle Hash Tree (IMHT), we can reduce overall costs of existing Merkle Hash Trees. We prove the security of the proposed scheme under the random oracle model, and further provide a comprehensive comparison between the proposed scheme and existing schemes. The theoretical analysis and experiment evaluation show that our scheme reduces both the communication and computation costs compared with existing schemes. The analysis also shows that the proposed scheme can achieve 99% verification probability by challenging only 90 data blocks. (C) 2021 Elsevier B.V. All rights reserved.

关键词 ：

Multiple replicas Cloud storage auditing Geographic location Merkle hash tree

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Variable selection has played a fundamental role in regression analysis. Spatial autoregressive model is a useful tool in econometrics and statistics in which context variable selection is necessary but not adequately investigated. In this paper, we consider conducting variable selection in spatial autoregressive models with a diverging number of parameters. Smoothly clipped absolute deviation penalty is considered to obtain the estimators. Moreover the dimension of the covariates are allowed to vary with sample size. In order to attenuate the bias caused by endogeneity, instrumental variable is adopted in the estimation procedure. The proposed method can do parametric estimation and variable selection simultaneously. Under mild conditions, we establish the asymptotic and oracle property of the proposed estimators. Finally, the performance of the proposed estimation procedure is examined via Monte Carlo simulation studies and a data set from a Boston housing price is analyzed as an illustrative example.

关键词 ：

Instrumental variable Oracle property Spatial autoregressive models Variable selection

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Using kernel methods, Lepski and Willer study a convolution structure density model and establish adaptive and optimal Lp risk estimations over an anisotropic Nikol'skii space (Lepski, O.; Willer, T. Oracle inequalities and adaptive estimation in the convolution structure density model. Ann. Stat. 2019, 47, 233-287). Motivated by their work, we consider the same problem over Besov balls by wavelets in this paper and first provide a linear wavelet estimate. Subsequently, a non-linear wavelet estimator is introduced for adaptivity, which attains nearly-optimal convergence rates in some cases.

关键词 ：

adaptive density estimation wavelet Besov space generalized deconvolution

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In this paper, we present an adaptive algorithm for maximizing a monotone nonsubmodular function with a cardinality constraint. Based on the relationship between OPT and the maximum marginal gain of the elements in the ground set, the algorithm first calculates all possible values of OPT, then computes in parallel a family of sets each of which corresponding to each value of OPT, and lastly selects the set with maximum value as the desired solution. For the first, we divide the value range of OPT into finite parts and take the lower bound of each part as a possible value of OPT. As the main ingredient for the parallel computation, we use the Bernoulli distribution to independently sample elements so as to ensure further parallelism. Provided the generic submodularity ratio (Formula Presented) of the monotone set function, we prove the algorithm deserves an approximation ratio (Formula Presented), consumes (Formula Presented) adaptive rounds, and needs (Formula Presented) oracle queries in expectation. Moreover, if the set function is submodular (i. e. (Formula Presented)), our algorithm can achieve an approximation guarantee (Formula Presented) coinciding with the state-of-art result. © 2020, Springer Nature Switzerland AG.

关键词 ：

Set theory Adaptive algorithms Approximation algorithms

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摘要 ：

本发明公开了一种混合优化BP神经网络的湖泊水质预测模型构建方法，首先从Oracle数据库中导出某市地表水的基本信息与水质监测数据，并用VBA语言将数据格式调整正确。然后使用SPSS删除数据空缺较多的行。完成预处理之后，再根据水环境知识筛选出实验相关的水质指标参数，接着分别利用BP神经网络、PSO‑BP神经网络、GA‑BP神经网络和GA‑PSO‑BP神经网络对水质指标参数预测进行模型构建，最后运用评估方法对构建模型进行评估。通过结合不同算法构建方法，充分考虑了各种算法的优缺点，结果表明基于遗传算法GA和粒子群算法PSO混合优化的BP神经网络模型能够提高水质指标参数预测准确率。

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