The sharing of Internet of Things (IoT) data plays an extensive role in our everyday lives. Exploring secure and efficient methods for data sharing is a prominent area of research. Proxy re-encryption (PRE) in the cloud provides a solution. However, traditional PRE schemes are vulnerable to algorithm substitution attacks (ASA). Moreover, in most PRE schemes, the proxy can know the relevant identity information of the data owner or the data recipient through the re-encryption key, and some current PRE schemes cannot guarantee strong resistance to collision. To address the limitations of traditional PRE schemes and offer a robust solution for secure and efficient data sharing in IoT application, we propose a reverse firewall for proxy re-encryption (PRE-RF) scheme, which is implemented by JPBC library. Security analysis shows that our PRE-RF scheme provides effective resistance to ASA, along with strong collision security, chosen plaintext attack security, and key-private security. Compared with similar state-of-the-art PRE schemes, our scheme reduces the length of the re-encryption keys and ciphertext, thereby reducing the storage cost of the system. Meanwhile, our scheme's computational cost is lower, thereby improving the operational efficiency of the system. Furthermore, the amount of time devoted to the reverse firewall in our scheme decreases as security level increases. As a result, these advantages make it a secure and efficient choice for sharing IoT data in unreliable cloud environments.

Privacy-preserving verifiable (outsourced) computation (PPVC) is a useful technique for a resource-constrained client to outsource computationally heavy but sensitive tasks to a computationally powerful but untrusted worker and to obtain expected correct results from the worker. In this paper, we analyze the privacy property of three matrix masking-based PPVC protocols, which have recently been published in IEEE Transactions on Cloud Computing [2] , [3] . To do this, we present a formal definition of a privacy model for a PPVC protocol (see Definition 1 ), and then prove that neither of those three PPVC protocols holds privacy under this model. We also review the comments by Cao et al. [1] on two of the three protocols and show an issue in their comments.

Recently, a privacy-preserving technique called Privacy-Preserving Matrix Transformation (PPMT) is widely used to construct efficient privacy-preserving Verifiable (outsourced) Computation (VC) protocols for specific functions. This technique is first proposed and formalized by Salinas et al. in 2015, and it enjoys provable privacy and high efficiency. Although it seems that Salinas et al.'s PPMT scheme and the further modified scheme are elegant, we still need to take a step back and precisely discuss whether the PPMT schemes are suitable choices for VC protocols. Since Salinas et al. gave two concrete PPMT schemes to achieve the matrix-related VC in data protection and proved that their schemes are private (in terms of indistinguishability), and Zhou et al. devised a new type of PPMT scheme for the same purpose, we focus on exploring privacy of these three types of PPMT schemes. In this paper, to achieve our object, we first propose the concept of a linear distinguisher and two constructions of the linear distinguisher algorithms. In particular, the linear distinguisher is a polynomial-time algorithm employed by an adversary to explore the privacy property of a cryptographic primitive. Then, we take these three PPMT schemes (including Salinas et al.'s original work, Yu et al.'s generalization and Zhou et al.'s variant) as targets and analyze their privacy property by letting an adversary make use of our linear distinguisher algorithms. The analysis results show that all these three types of transformations do not hold privacy even against passive eavesdropping (i.e., a ciphertext-only attack), and subsequently, the privacy-preserving VC protocols, based on any of these PPMT schemes, also do not hold the same privacy.

Outsourcing encrypted data and query services to clouds have been widely adopted by data owners, such as in the energy, healthcare, and transportation sectors, usually for economic considerations. However, this is usually prone to data privacy breaches. In smart grids, although many privacy-preserving data query solutions have been presented in the literature, they either suffer from low query efficiencies or limited capabilities of statistics queries (say the aggregation, variance, or extrema only). To mitigate this gap, in this paper we propose an efficient and privacy-preserving statistics query scheme over encrypted data in smart grids, coined EPPSQ, which can achieve diverse statistics queries in an efficient and privacy-preserving way. Specifically, we first develop a public key encryption based secure data collection protocol for data owners. Then, we design a two-server model based privacy-preserving statistics query protocol for data requesters. In addition, four algorithms respectively for securely computing the statistics (i.e., max/min, variance, mean, and count) over the encrypted data in smart grids are crafted. Security analysis shows that the proposed EPPSQ scheme can effectively guarantee data privacy while outsourcing the encrypted data and query services in smart grids. Further, extensive experiments demonstrate that the proposed EPPSQ scheme outperforms existing CKKS-based or BFV-based studies in terms of computational efficiency.

High pressure is a powerful tool in material sciences which can lead to the discovery of novel inorganic species in high oxidation states. Based on the prediction of the stability of PdF6 with a high Pd oxidation state of +6, we propose three potential guiding rules for finding stable transition metal (TM) fluorides with high +6 oxidation states: (1) the existence of a large (>7 eV) valence orbitals energy differences of atoms between the TM d orbital and the F 2p orbital; (2) an appropriate number of valence electrons within the range of 6-11; and (3) suitable electronegativity values less than 2.3 on the Pauli scale. More importantly, by synergistically invoking all of these rules, we predict, by combining a particle swarm optimization algorithm with first-principles calculation on the phase stabilities of the various TM-F compounds, a collection of new TMF6 species with the space group Pnma that have a +6 oxidation state. Subsequently, we develop an understanding of the high +6 oxidation state for the TM elements. These findings are expected to play a crucial role in the predictive discoveries of new fluorides with high oxidation states of +6.

The early detection of biomarker proteins in clinical samples is of great significance for the diagnosis of diseases. However, it is still a challenge to detect low-concentration protein. Herein, a label-free aptamer-based amplification assay, termed the ATC-TA system, that allows fluorescence detection of very low numbers of protein without time-consuming washing steps and pre-treatment was developed. The target induces a conformational change in the allosteric aptasensor, triggers the target cycling and transcription amplification, and ultimately converts the input of the target protein into the output of the light-up aptamer (R-Pepper). It exhibits ultrahigh sensitivity with a detection limit of 5.62 fM at 37 ℃ and the accuracy is comparable to conventional ELISA. ATC-TA has potential application for the detection of endogenous PDGF-BB in serum samples to distinguish tumor mice from healthy mice at an early stage. It also successfully detects exogenous SARS-CoV-2 spike proteins in human serum. Therefore, this high-sensitive, universality, easy-to-operate and cost-effective biosensing platform holds great clinical application potential in early clinical diagnosis.

Visualizing the dynamics of ATP in living cells is key to understanding cellular energy metabolism and related diseases. However, the live-cell applications of current methods are still limited due to challenges in biological compatibility and sensitivity to pH. Herein, a novel label-free fluorescent " turn-on " biosensor for monitoring ATP in living bacterias and mammalian cells was developed. This biosensor (Broc-ATP) employed heterobifunctional aptamers to detect ATP with high sensitivity in vitro. In our system, a very useful tandem method was established by combining four Broc-ATPs with 3 × F30 three-way junction scaffold to construct an intracellular biosensor that achieves sufficient fluorescence to respond to intracellular ATP. This intracellular biosensor can be used for sensitive and specific dynamic imaging of ATP in mammalian cells. Hence, this genetically encoded biosensor provides a robust and efficient tool for the detection of intracellular ATP dynamics and 3 × F30 tandem method expands the application of heterobifunctional aptamers in mammalian cells.

The wireless body area network (WBAN) provides users with real-time medical services. Meanwhile, the cloud technology provides greater storage space and computing power for medical data. Both of them have contribute to the development of telemedicine. In a cloud-assisted WBAN, the open network environment and the semi-trust cloud service providers expose the user’s private medical data to backdoor adversaries who can make exfiltration attacks, such as the algorithm substitution attack (ASA) through the process of data sharing. Therefore, it is necessary to find a secure and efficient medical data sharing scheme for the huge amount of medical data. In this paper, we first design an identity-based proxy re-encryption scheme with cryptographic reverse firewall (IBPRE-CRF), then show the application in a multiple-access telemedicine data sharing scenario. Security analysis shows that the IBPRE-CRF scheme provides chosen plaintext attack security and resists exfiltration attacks. Performance analysis shows that the IBPRE-CRF scheme has a significant communication and computational cost advantage while being resistant to exfiltration attacks in clouds. Therefore, our IBPRE-CRF scheme is suitable for telemedicine data sharing in a cloud-assisted WBAN.

In this paper we present an optimized variant of Gentry, Halevi and Vaikuntanathan (GHV)’s Homomorphic Encryption (HE) scheme. Our scheme is appreciably more efficient than the original GHV scheme without losing its merits of the (multi-key) homomorphic property and matrix encryption property. In this research, we first measure the density for the trapdoor pairs that are created by using Alwen and Peikert’s trapdoor generation algorithm and Micciancio and Peikert’s trapdoor generation algorithm, respectively, and use the measurement result to precisely discuss the time and space complexity of the corresponding GHV instantiations. We then propose a generic GHV-type construction with several optimizations that improve the time and space efficiency from the original GHV scheme. In particular, our scheme can achieve asymptotically optimal time complexity and avoid generating and storing the inverse of the used trapdoor. Finally, we present an instantiation that, by using a new set of (lower) bound parameters, has the smaller sizes of the key and ciphertext than the original GHV scheme.

在疫情危机中孕育而成的"抗疫精神",是中华民族精神和时代精神的融合,是中国精神的具体表现样态,极具高校思想政治教育价值.将抗疫精神融入高校思想政治教育,能够培养大学生的爱国情怀、团结作风、奋斗精神和担当品格.高校应主动将抗疫精神融入课程体系、校园文化、网络新媒体传播、科研和实践中,引导大学生勇担时代重任,成为社会主义合格建设者和接班人.

冤假错案的平反和"自白任意性"原则的确立,有效规制了刑讯逼供、威胁等暴力取证方法的使用,但以引诱为典型代表,带有强烈心理或精神强制色彩的"隐性暴力"取证方法日益受到办案机关青睐,与其他取证方法并用,以"组合拳"的形式作为收集证据的"一线手段".但受限于"引诱"取证的非暴力强制性和权利侵害的隐蔽性,其常被视为侦查谋略,一直未能引起我国刑事诉讼法学界的重视,立法层面也未能给予积极回应,因而导致实践中采取"引诱"方法收集的证据面临界定难、取证难、排除难的三大困境.何谓"引诱"以及如何将其与威胁、欺骗等概念相区别,是适用非法证据排除规则处理非法引诱的逻辑前提.结合我国刑事诉讼法关于"引诱"规制立场的历次变迁,参考德国刑事诉讼法及我国台湾地区刑事诉讼的理论与实践,建议将《刑事诉讼法》第52条规定的"引诱"解释为"利诱",以回应实务中难以认定引诱的困惑,助力于非法证据排除规则的全面落地.

当前城市电网容量增长空间有限,清洁能源渗透的随机性及负荷分布的不均衡性,导致城市电网输电阻塞频繁发生;110kV高压配电网检修及运行方式调整受限其灵活调节能力,转供时序操作难以保证系统可靠安全供电.相较于中低压配电网,高压配电网拓扑重构具备广域时间尺度上的大范围潮流转移能力,协同储能电站灵活的充放电特性,可有效解决上述问题.因此提出基于模型预测控制方法,将转供0-1离散控制与储能荷电状态(state of charge,SOC)连续调节相结合,构建高压配电网转供与储能电站协同控制的日前-日内优化模型;日前计划为日内调度提供参考;日内调度采用滚动优化校正方式,精细化调控储能电站出力以降低转供时序操作风险.算例结果表明,所提模型可充分挖掘2种调控方式不同时间尺度的调控潜力,达到消除阻塞、降低避峰切负荷风险,提高能源消纳水平的目的;同时结合提前预防、分步转供的操作原则,在保证运行低风险的同时指导高压配电网检修及运行方式调整.

The rapid convergence of legacy industrial infrastructures with intelligent networking and computing technologies (e.g., 5G, software-defined networking, and artificial intelligence), have dramatically increased the attack surface of industrial cyber-physical systems (CPSs). However, withstanding cyber threats to such large-scale, complex, and heterogeneous industrial CPSs has been extremely challenging, due to the insufficiency of high-quality attack examples. In this article, we propose a novel federated deep learning scheme, named DeepFed, to detect cyber threats against industrial CPSs. Specifically, we first design a new deep learning-based intrusion detection model for industrial CPSs, by making use of a convolutional neural network and a gated recurrent unit. Second, we develop a federated learning framework, allowing multiple industrial CPSs to collectively build a comprehensive intrusion detection model in a privacy-preserving way. Further, a Paillier cryptosystem-based secure communication protocol is crafted to preserve the security and privacy of model parameters through the training process. Extensive experiments on a real industrial CPS dataset demonstrate the high effectiveness of the proposed DeepFed scheme in detecting various types of cyber threats to industrial CPSs and the superiorities over state-of-the-art schemes.

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

Private Information Retrieval (PIR) allows a client to privately retrieve some data from a public database. There exist two types of PIR: (computational) Single-server PIR (SPIR) and (information-theoretic) multi-server PIR. In this paper, we focus on exploring SPIR. We first propose a simple and efficient additively-homomorphic encryption scheme of which privacy is based on the learning with binary errors assumption that is known as an interesting candidate for practical lattice-based cryptography. Then, according to our proposed homomorphic encryption scheme, we give a Verifiable (single/multi-bit) SPIR (VSPIR) scheme for the single-query case under the malicious server model. To the best of our knowledge, our proposal is the first practical non-interactive VSPIR scheme employing an efficient probabilistic proof that can discover the forged result with overwhelming probability. The corresponding communication complexity and computational complexity are comparable with those of some typical SPIR schemes. Moreover, we extend our single-query VSPIR scheme to construct a non-interactive multi-query solution. In particular, the corresponding communication complexity and computational complexity are the same as those of the single-query scheme. Finally, we provide detailed implementation results to confirm efficiency of our proposals.

Phosphorene has broad application prospects for adsorption due to the unique combination of ultrahigh surface-volume ratio and high chemical reactivity. The adsorption behavior of rare gases (He, Ne, Ar, Kr and Xe) on pristine and doped phosphorene systems were studied with first-principle calculation. The adsorption energies of rare gases on pristine phosphorene are comparable to graphene. The electronic structure of phosphorene can be widely tuned by doping heteroatoms. The adsorption abilities of rare gas on doped phosphorene are significantly enhanced, in particular for Li-doped phosphorene. The phosphorene with surface decorated is a promising material for capture of noble gases, especially for the radioactive Kr and Xe.

The innovative development of data technology makes the social operation more efficient.At the same time, we should also realize that there are some hidden dangers in the collection, storage, analysis and research of data information.Therefore, we need to fully protect the autonomy of citizens' personal data from the dual aspects of law and business management to make a win-win situation.

The prediction of different shear stresses is one of the great challenges of turbulent–turbulentstratified two-phase flow in horizontal pipes. In this work, VOF method, near-wall differential viscosity and local turbulence viscosity distribution coefficient function are introduced and offer an efficient tool to correct the interface turbulence viscosity. The results show that the new method can better predict the shear stresses, liquid holdup and pressure drop of stratified two-phase flow. The fitting relationship between interfacial and wall friction factor (fi∕fW) is in good agreement with the experimental data. It is found that fi∕fW is predicted with a relative error of 12.62% by the new method, which is much less than that by any other method when the gas and liquid superficial Reynolds numbers are 8000≤ ReSG ≤90000 and 5000≤ ReSL ≤170000. It provides a reliable method for achieving the closure of stratified flow to predict the shear stresses.

Recent experiments on the reaction of metal-doped Rh clusters with NO indicate that V-doping is the most effective for the NO decomposition. The reaction mechanism of NO molecules on Rh5V+ cations was studied using density functional theory calculations in the present work. The first NO molecule adsorbs on the Rh5V+ cluster and decomposes into O and N atoms to form the V-O bond, with a low barrier of 19.76 kcal/mol. The V-O affinity strongly promotes NO dissociation. There are two alternative reaction mechanisms when the second NO reacts with the N atom, which results in the release of the N-2 molecule. One reaction mechanism is that the second NO adsorbs on one Rh atom, then the N-O bond breaks, and a N-N bond forms with an activation barrier of 16.89 kcal/mol. Another reaction mechanism is that the N-O bond of the intermediate N2O ruptures and N-2 is released. The value of the activation barrier is only 0.16 kcal/mol. Our results indicate that V-doping Rh clusters can improve chemical activity because of the strong V-O affinity, conforming to the experimental results.

为了解决现有光伏电站短期发电量预测方法存在的预测模型复杂、预测误差较大、泛化能力较低的问题,提出一种基于深度信念网络的短期发电量预测方法.首先综合考虑影响光伏出力的环境因素和光伏板的运行参数以及光伏电站历史发电量数据,对深度信念网络进行训练和学习.在此基础上,采用重构误差的方法确定深度信念网络隐含层层数.最后针对某光伏电站短期发电量进行预测算例分析,验证了该预测模型能主动选择样本抽象特征、自动确定隐含层层数,对短期发电量预测精度较高.对比前馈反向传播(Back Propagation,BP)神经网络预测模型与长短期记忆网络(Long/Short Term Memory,LSTM)预测模型,结果表明所提方法运算量低、预测精度高,且增加神经网络的深度比改进神经网络神经元对预测效果更有效.