A Proxy Signature Scheme as Secure as Decisional Diffie-Hellman Problem

Based on decisional Diffie-Hellman problem, we propose a simple proxy-protected signature scheme. In the random oracle model, we also carry out the strict security proof for the proposed scheme. The security of the proposed scheme is not loosely related to the discrete logarithm assumption but tightly related to the decisional Diffie-Hellman assumption in the random oracle model.

Probabilistic Analysis and Multicriteria Decision for Machine Assignment Problem with General Service Times

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Approximate Dynamic Programming for Stochastic Resource Allocation Problems

A stochastic resource allocation model, based on the principles of Markov decision processes(MDPs), is proposed in this paper. In particular, a general-purpose framework is developed, which takes into account resource requests for both instant and future needs. The considered framework can handle two types of reservations(i.e., specified and unspecified time interval reservation requests), and implement an overbooking business strategy to further increase business revenues. The resulting dynamic pricing problems can be regarded as sequential decision-making problems under uncertainty, which is solved by means of stochastic dynamic programming(DP) based algorithms. In this regard, Bellman’s backward principle of optimality is exploited in order to provide all the implementation mechanisms for the proposed reservation pricing algorithm. The curse of dimensionality, as the inevitable issue of the DP both for instant resource requests and future resource reservations,occurs. In particular, an approximate dynamic programming(ADP) technique based on linear function approximations is applied to solve such scalability issues. Several examples are provided to show the effectiveness of the proposed approach.

Airport gate assignment problem with deep reinforcement learning

With the rapid development of air transportation in recent years,airport operations have attracted a lot of attention.Among them,airport gate assignment problem(AGAP)has become a research hotspot.However,the real-time AGAP algorithm is still an open issue.In this study,a deep reinforcement learning based AGAP(DRL-AGAP)is proposed.The optimization object is to maximize the rate of flights assigned to fixed gates.The real-time AGAP is modeled as a Markov decision process(MDP).The state space,action space,value and rewards have been defined.The DRL-AGAP algorithm is evaluated via simulation and it is compared with the flight pre-assignment results of the optimization software Gurobiand Greedy.Simulation results show that the performance of the proposed DRL-AGAP algorithm is close to that of pre-assignment obtained by the Gurobi optimization solver.Meanwhile,the real-time assignment ability is ensured by the proposed DRL-AGAP algorithm due to the dynamic modeling and lower complexity.

Multi-attribute group decision making method under 2-dimension uncertain linguistic variables

A method is proposed to deal with the uncertain multiple attribute group decision making problems,where 2-dimension uncertain linguistic variables(2DULVs)are used as the reliable way for the experts to express their fuzzy subjective evaluation information.Firstly,in order to measure the 2DULVs more accurately,a new method is proposed to compare two 2DULVs,called a score function,while a new function is defined to measure the distance between two 2DULVs.Secondly,two optimization models are established to determine the weight of experts and attributes based on the new distance formula and a weighted average operator is used to determine the comprehensive evaluation value of each alternative.Then,a score function is used to determine the ranking of the alternatives.Finally,the effectiveness of the proposed method is proved by an illustrated example.

Scheduling problem for hot-rolling seamless steel tube production

Taking the seamless tube plant of Baoshan Iron & Steel Complex in China as the background,we analyze the characters of hot rolling seamless steel tube:multi varieties,low volume,complicated production process,flexible production routes.Then integrated scheduling problem for hot rolling seamless steel tube production is studied,which covers two key points;order-grouping problem and solution method for flowshop/jobshop scheduling problem.On the basis of these two problems,integrated scheduling decision system is developed.The design idea,function flow sheet,data processing method,and functional module of visualized human-computer interactive scheduling system implemented in seamless steel tube plant of Shanghai Baoshan Iron & Steel Complex are described into detail.Compared with manual system,the performance of system shows the applicability and superiority in several criteria.

Constraints Based Decision Support for Site-Specific Preliminary Design of Wind Turbines

This study presents a decision-support tool for preliminary design of a horizontal wind turbine system. The function of this tool is to assist the various actors in making decisions about choices inherent to their activities in the field of wind energy. Wind turbine cost and site characteristics are taken into account in the used models which are mainly based on the engineering knowledge. The present tool uses a constraint-modelling technique in combination with a CSP solver (numerical CSPs which are based on an arithmetic interval). In this way, it generates solutions and automatically performs the concept selection and costing of a given wind turbine. The data generated by the tool and required for decision making are: the quality index of solution (wind turbine), the amount of energy produced, the total cost of the wind turbine and the design variables which define the architecture of the wind turbine system. When applied to redesign a standard wind turbine in adequacy with a given site, the present tool proved both its ability to implement constraint modelling and its usefulness in conducting an appraisal.

Model for Solving Fuzzy Multiple Objective Problem

In real world decision making problems, the decision maker has to often optimize more than one objective, which might be conflicting in nature. Also, it is not always possible to find the exact values of the input data and related parameters due to incomplete or unavailable information. This work aims at developing a model that solves a multi objective distribution programming problem involving imprecise available supply, forecast demand, budget and unit cost/ profit coefficients with triangular possibility distributions. This algorithm aims to simultaneously minimize cost and maximize profit with reference to available supply constraint at each source, forecast demand constraint at each destination and budget constraint. An example is given to demonstrate the functioning of this algorithm.

A New Proof for the Tight Range of Optimal Order Quantities for the Newsboy Problem with Mean and Standard Deviation

In the classical Newsboy problem, we provide a new proof for the tight range of optimal order quantities for the newsboy problem when only the mean and standard deviation of demand are available. The new proof is only based on the definition of the optimal solution therefore it is the most straightforward method. It is also shown that the classical Scarf’s rule is the mid-point of the range of optimal order quantities. This provides an additional understanding of Scarf’s order rule as a distribution free decision.

Application of Fuzzy Optimization Method in Decision-Making for Personnel Selection

The paper describes the necessity of application of intelligent technologies to support decisions of more objective problems in human resource management. In this paper, we describe the methodology for personnel selection problem for the vacancy with regard to the importance and nonequivalence of numerous indicators characterizing the alternatives. The specific features of the selection problem are highlighted, immersing the problem into a fuzzy environment. A fuzzy multicriterial model of the personnel selection problem is proposed. A technique of order preference by similarity to ideal solition (TOPSIS), was applied for evaluation and regulation of alternatives. This technique is based on criteria of qualitative character, which are hierarchically structured by multiple experts to intellectually support decisions made in personnel selection problem. Using TOPSIS method and generated criteria system an experiment was conducted for evaluation of the candidates during solution of hiring problems. The obtained and reviewed results were compared with results obtained using in reality.

一般存取结构上可公开验证的多级秘密共享

可公开验证的秘密共享允许任何人仅从公开信息中发现分发者或参与者的欺诈行为。为扩展多秘密共享应用范围,首先提出一个可公开验证的多级秘密共享(PVMSSS)方案模型,而后基于单调张成方案及安全多方计算,构造一般存取结构上可公开验证多用的可更新的多级秘密共享方案。秘密分发阶段,方案中各参与者秘密份额由自己计算,分发者不需向参与者传送任何秘密信息,且每个参与者只需维护一个秘密份额即可实现对多个秘密的重构。利用双线性对的性质,任何人均可验证更新前后秘密份额的正确性及公开信息的有效性,从而有效防止分发者和参与者的欺诈。秘密重构阶段,利用安全多方计算构造伪份额,保证每个参与者的真实份额永远不会暴露,实现了份额的多用性。在秘密的每一次更新中,分发者只需公布更新临时份额的相应公开信息,即可实现对参与者秘密份额的更新。最后对方案的正确性和安全性进行详细分析,在计算Diffie-Hellman和判定双线性Diffie-Hellman问题及假设下,该方案是可证明安全的。

A UAV collaborative defense scheme driven by DDPG algorithm

The deep deterministic policy gradient(DDPG)algo-rithm is an off-policy method that combines two mainstream reinforcement learning methods based on value iteration and policy iteration.Using the DDPG algorithm,agents can explore and summarize the environment to achieve autonomous deci-sions in the continuous state space and action space.In this paper,a cooperative defense with DDPG via swarms of unmanned aerial vehicle(UAV)is developed and validated,which has shown promising practical value in the effect of defending.We solve the sparse rewards problem of reinforcement learning pair in a long-term task by building the reward function of UAV swarms and optimizing the learning process of artificial neural network based on the DDPG algorithm to reduce the vibration in the learning process.The experimental results show that the DDPG algorithm can guide the UAVs swarm to perform the defense task efficiently,meeting the requirements of a UAV swarm for non-centralization,autonomy,and promoting the intelligent development of UAVs swarm as well as the decision-making process.

标准模型下可证安全的基于身份门限签密方案

提出了一种有效的基于身份的门限签密方案.采用双线性对映射技术,基于Waters和Paterson提出的签名方案,设计了标准模型下可证安全的门限签密方案,并利用决策双线性Diffie-Hellman问题的困难性证明了方案的语义安全性,同时利用计算Diffie-Hellman问题的困难性证明了方案的不可伪造性.相对随机预言模型下的同类方案,本文方案更具有实际意义.

标准模型下可证安全的基于身份的门限环签密方案

签密是一个能够同时提供认证性和保密性的密码学术语,而它却比分别签名和加密具有更低的计算成本。环签密除具有签密的一般属性外,还具有匿名性。为了设计基于身份的门限环签密方案,利用秘密共享和双线性对技术,提出了一种标准模型下基于身份的门限环签密方案,并对方案的安全性进行了分析。最后,通过计算Diffie-Hell-man问题和判定性Diffie-Hellman问题的困难性,证明了该方案在适应性选择消息和身份攻击下的不可伪造性以及在适应性选择密文攻击下的不可区分性。

一种可证安全的基于身份门限代理签密方案

代理签名可以实现签名权利的委托,具有可认证性和不可否认性,然而却不能提供保密性。签密是一种将加密和签名结合在一起的技术,它同时具有两者的优点。利用双线性对技术,提出了一种在标准模型下有效的基于身份门限代理签密方案,并对方案的安全性进行了分析。最后,通过DBDH问题的困难性证明了方案的语义安全性,同时利用CDH问题的困难性证明了方案的不可伪造性。

密文长度固定的基于身份环签密方案

利用双线性对技术提出了一种无须随机预言机的基于身份环签密方案。密文的大小是一个常量,并且与环的大小无关。通过引入了选择身份和选择消息攻击的安全模型,利用DHI问题的困难性,证明了方案的不可伪造性,同时利用DBDHE问题的困难性,证明了方案在选择身份和选择密文攻击下的不可区分性。与其他的环签密方案相比,该方案具有较高的效率。

NTRU格上无证书加密

为降低密钥尺寸,利用陷门抽样算法在优选的NTRU格上抽取部分私钥并使用多项式环上带误差的学习问题计算公钥等方法来构造格上无证书加密方案。它的安全性基于多项式环上带误差学习的判定问题和小多项式比判定问题等两个困难问题假设。为获取更好的效率,该文还提出一个无证书并行加密方案。该方案用中国剩余定理分解扩大后的明文空间为多个不同素理想之积来实现并行加密。它还用中国剩余定理分解加密运算所在的多项式环获取中国剩余基来优化算法,使算法只涉及整数间运算。结果显示该方案具有计算和通信复杂度低等特点。

一种可证明安全的基于身份的高效环签密方案

为了克服传统公钥基础设施中的证书管理以及提高环签密方案的安全性,提出一种有效的基于身份的环签密方案。通过使用双线性对技术,设计一个在标准模型下基于身份的环签密方案,并分析方案的正确性进行。通过两个困难问题假设CDH问题和DBDH问题,证明方案满足不可伪造性和不可区分性,同时通过与几个现有方案进行比较,指出该方案具有较高的效率。

基于Waters的ID加密的高效选择密文安全公钥密码体制

2004年的欧密会上,Canetti,Halevi和Katz提出了将Selectivre-ID安全的基于身份加密方案转化为选择密文安全(即,CCA安全)的公钥加密方案的方法。但由于该方法需要用到一次性签名,给所基于的方案增加了明显的通信和计算负载。该文由Waters提出的Adaptive-ID安全的基于身份加密(IDE)方案构造了一个新的CCA安全公钥加密方案。这里的“身份”由前两部分密文的hash值得到,密文合法性由双线性映射来验证。其效率比直接利用CHK 的一般转化得到方案有明显提高。新方案的安全性在标准的决定性双线性Diffie-Hellman假设下被证明。

高效率的非交互OT_n^k协议及其应用

在Cheng-Kang Chu和Wen-Guey Tzeng设计的OTnk协议的基础上提出了一个非交互的OTkn协议.该协议降低了通信复杂度和计算复杂度,接收方的安全性是无条件的,发送方的安全性在判定Diffie-Hellman问题假设下是计算安全的;相比Cheng-Kang Chu和Wen-Guey Tzeng的OTnk协议效率更高.同时修正和给出协议安全性的完整证明.基于所提出的非交互OTnk协议设计了一个数字产品秘密交易机制,解决了产品价格不一致时的数字产品交易问题.