More and more studies begin to focus on emotional conversation generation. However, the existing studies tend to focus only on emotional factors and ignore the relevance and diversity of topics in dialogues, as well as the emotional tendency closely related to topics, which may lead to the quality decline of generated responses. Therefore, a topic-expanded emotional conversation generation model that integrated topic information and emotional factors was proposed. Firstly, the conversation context was globally-encoded, the topic model was introduced to obtain the global topic words, and the external affective dictionary was used to obtain the global affective words in this model. Secondly, the topic words were expanded by semantic similarity and the topic-related affective words were extracted by dependency syntax analysis in the fusion module. Finally, the context, topic words and affective words were input into a decoder based on the attention mechanism to prompt the decoder to generate topic-related emotional responses. Experimental results show that the model can generate rich and emotion-related responses. Compared with the model Topic-Enhanced Emotional Conversation Generation(TE-ECG), the proposed model has an average increase of 16.3% and 15.4% in unigram diversity(distinct-1) and bigram diversity(distinct-2); and compared with Seq2SeqA(Sequence to Sequence model with Attention), the proposed model has an average increase of 26.7% and 28.7% in unigram diversity(distinct-1) and bigram diversity(distinct-2).

Aiming at the problem of data being easily copied and the realization of data confidentiality in current data trading process, a data trading scheme based on blockchain and trusted computing was proposed. First, the blockchain was applied to record data information, trading information and data usage records, which facilitated to confirm the rights of data assets and data provenance. Then, the trusted computing and encryption algorithms were used to ensure the security of the trading data transmission. Finally, the algorithms provided by the data owners and demanders were applied to complete the calculation in the trusted computing environment, after that, the results were output and encrypted to return to the demanders. In the proposed scheme, the demanders can use the data for calculation without revealing data from the data subjects, and the transmission security is guaranteed through trusted encryption.

In order to use computer vision technology to accurately detect the fall of the elderly, aiming at the incompleteness of existing fall detection algorithms caused by artificial designing of features and the problems in the fall detection process such as the difficulty of separating foreground and background, the confusion of objects, the loss of moving objects, and the low accuracy of fall detection, a deep learning fall detection algorithm with the fusion of human motion information was proposed to detect the fall state of human body. Firstly, foreground and background were separated by the improved YOLOv3 network, and human object was marked by minimum bounding rectangle according to the detection results of YOLOv3 network. Then, by analyzing the motion features in the process of human fall, the motion features of human body were vectorized and transformed into the motion weight information between 0 and 1 through the Sigmoid activation function. Finally, in order to classify human falls, the motion features and the features extracted by Convolutional Neural Network (CNN) were spliced and fused through the fully connected layer. The proposed fall detection algorithm was compared with human object detection algorithms such as background difference, Gaussian mixture, VIBE (VIsual Background Extractor), Histogram of Oriented Gradient (HOG) and human fall judgment schemes such as threshold method, grading method, Support Vector Machine (SVM) classification, CNN classification, and tested under different lighting conditions and the interference of mixed daily noise motion. The results show that the proposed algorithm is superior to traditional human fall detection algortihms in environmental adaptability and fall detection accuracy. The proposed algorithm can effectively detect the human body in the video and accurately detect the fall state of human body, which further verifies the feasibility and efficiency of the deep learning recognition method with the fusion of motion information in the video fall behavior analysis.

In order to solve the model drift problem of Multi-Domain convolutional neural Network (MDNet) when the target moves rapidly and the appearance changes drastically, a Multi-Domain convolutional neural Network based on Self-Attention (SAMDNet) was proposed to improve the performance of the tracking network from the dimensions of channel and space by introducing the self-attention mechanism. First, the spatial attention module was used to selectively aggregate the weighted sum of features at all positions to all positions in the feature map, so that the similar features were related to each other. Then, the channel attention module was used to selectively emphasize the importance of interconnected channels by aggregating all feature maps. Finally, the final feature map was obtained by fusion. In addition, in order to solve the problem of inaccurate classification of the network model caused by the existence of many similar sequences with different attributes in training data of MDNet algorithm, a composite loss function was constructed. The composite loss function was composed of a classification loss function and an instance discriminant loss function. First of all, the classification loss function was used to calculate the classification loss value. Second, the instance discriminant loss function was used to increase the weight of the target in the current video sequence and suppress its weight in other sequences. Lastly, the two losses were fused as the final loss of the model. The experiments were conducted on two widely used testing benchmark datasets OTB50 and OTB2015. Experimental results show that the proposed algorithm improves success rate index by 1.6 percentage points and 1.4 percentage points respectively compared with the champion algorithm MDNet of the 2015 Visual-Object-Tracking challenge (VOT2015). The results also show that the precision rate and success rate of the proposed algorithm exceed those of the Continuous Convolution Operators for Visual Tracking (CCOT) algorithm, and the precision rate index of it on OTB50 is also superior to the Efficient Convolution Operators (ECO) algorithm, which verifies the effectiveness of the proposed algorithm.

In order to overcome the difficulty in discovering multi-stage attack from multi-source alerts, an algorithm was proposed to mine the attack sequence pattern. The multi-source alerts were normalized into a unified format by matching them with regular expressions. The redundant information of alerts was compressed, and the alerts of the same stage were clustered according to the association rule set trained by strong association rules, efficiently removing the redundant alerts, so that the number of alerts was reduced. Then, the clustered alerts were divided to obtain candidate attack event dataset by sliding-window, and the attack pattern mining algorithm PrefixSpan was used to find out the attack sequence patterns of multi-stage attack events. The experimental results show that the proposed algorithm can lead to an accurate and efficient analysis of alert correlation and extract the attack steps of attack events without expert knowledge. Compared with the traditional algorithm PrefixSpan, the algorithm has an increase in attack pattern mining efficiency of 48.05%.

For the segmentation of images with intensity inhomogeneity, a region-adaptive intensity fitting model combining global information was proposed. Firstly, the local and global terms were constructed based on local and global image information respectively. Secondly, an adaptive weight function was defined to indicate the deviation degree of the gray scale of a pixel neighborhood by utilizing the extreme difference level in the pixel neighborhood. Finally, the defined weighting function was used to assign weights to local and global terms adaptively to obtain the energy functional of the proposed model and the iterative equation of the model's level set function was deduced by the variational method. The experimental results show that the proposed model can segment various inhomogeneous images stably and accurately in comparison with Region-Scalable Fitting (RSF) model and Local and Global Intensity Fitting (LGIF) model, which is more robust in the position, size and shape of initial contour of evolution curve.

Since the vehicles provided by current online car rental platforms are in the fixed price, there are some issues coming up such as unreasonable allocation of the vehicle resources, unreliable price that could not indicates the real market supply and demand timely, and generally low social welfare. Therefore, an auction based vehicle allocation and pricing mechanism for car rental was proposed. Firstly, a mathematical model and a social welfare maximization objective function were established by studying the model of online car rental issues. Secondly, based on the minimum cost and maximum flow algorithm, the optimal vehicle resource allocation algorithm was adopted among the rental vehicle allocation algorithms. Finally, in terms of the price calculation algorithms, a truthful VCG (Vickrey-Clarke-Groves) price algorithm was used to calculate the final price. As a result, compared with the traditional first-come-first-serving algorithms, the order success rate of the proposed scheme was increased by 20% to 30%, and the revenue was increased by about 30%. Theoretical analysis and experiment results show that the proposed mechanism has the advantages of optimizing vehicle allocation and flexible price strategy.

Compressive Data Gathering (CDG) using the combination of Compressed Sensing (CS) theory and sparse random projection for Wireless Sensor Network (WSN) can greatly reduce the amount of data transmitted over the network. Aiming at the unstable and unbalanced problems of the overall energy consumption of network caused by selecting the projection nodes randomly as cluster heads to collect data, two new compresseive data gathering methods of balanced projection nodes were proposed. For WSN with uniform distribution of nodes, an even clustering method based on spatial location was proposed. Firstly, the grids were evenly divided. Then, the projection nodes were selected in each grid for clustering according to the shortest distance principle. Finally, the intra-cluster data was collected by the projection nodes to the sink node for completing the data collection, so that the projection nodes were distributed evenly and the network energy consumption was balanced. For WSN with uneven distribution of nodes, an even clustering method based on node density was proposed. The locations and densities of nodes were taken into account together, for the grid with small number of nodes, the projection nodes were no longer selected, and the few nodes in the grid were allocated to the adjacent grids, which balanced the network energy and prolonged the network lifetime. The simulation results show that, compared with the random projection node method, the network lifetime of the proposed two methods is extended by more than 25%, and the number of remaining nodes can reach about 2 times in the middle stage of network running. The proposed two methods have better network connectivity and increase the overall network lifetime significantly.

To resist mobile attacks, a new dynamic threshold signature scheme based on Chinese Remainder Theorem (CRT) was proposed. Firstly, members exchanged their shadows to generate their private keys and the group public key. Secondly, a partial signature was generated by cooperation. Finally, the partial signature was used to synthesize the signature. The scheme does not expose the group private key in the signature process, so that the group private key can be reused. The members update their private keys periodically without changing the group public key to ensure that the signature is still valid before update. Besides, the scheme allows new members to join while keeping the old member's private keys and group private key unexposed. The scheme has forward security, which can resist mobile attacks effectively. Theoretical analysis and simulation results show that, compared with the proactive threshold scheme based on Lagrange interpolation, the updating time consumption of the proposed scheme is constant, therefore the scheme has time efficiency.

K-means algorithm is a widely used clustering algorithm, but the selection of the initial clustering centers in the traditional K-means algorithm is random, which makes the algorithm easily fall into local optimum and causes instability in the clustering result. In order to solve this problem, the idea of multi-dimensional grid space was introduced to the selection of initial clustering center. Firstly, the sample set was mapped to a virtual multi-dimensional grid space structure. Secondly, the sub-grids containing the largest number of samples and being far away from each other were searched as the initial cluster center grids in the space structure. Finally, the mean points of the samples in the initial cluster center grids were calculated as the initial clustering centers. The initial clustering centers chosen by this method are very close to the actual clustering centers, so that the final clustering result can be obtained stably and efficiently. By using computer simulation data set and UCI machine learning data sets to test, both the iterative number and error rate of the improved algorithm are stable, and smaller than the average of the traditional K-means algorithm. The improved algorithm can effectively avoid falling into local optimum and guarantee the stability of clustering result.

Under MapReduce computing scenarios, complex data mining algorithms typically require multiple MapReduce jobs' collaboration process to compete the task. However, serious redundant disk read and write and repeat resource request operations among multiple MapReduce jobs seriously degrade the performance of the algorithm under MapReduce. To improve the computational efficiency of ItemBased recommendation algorithm, firstly, the performance issues of the ItemBased collaborative filtering algorithm under MapReduce platform were analyzed. Secondly, the execution efficiency of the algorithm was improved by taking advantage of Spark's performance superiority on iterative computation and memory computing, and the ItemBased collaborative filtering algorithm under Spark platform was implemented. The experimental results show that, when the size of the cluster nodes is 10 and 20, the running time of the algorithm in Spark is only 25.6% and 30.8% of that in MapReduce. The algorithm's overall computing efficiency of Spark platform improves more than 3 times compared with that of MapReduce platform.

In order to have a comprehensive understanding and evaluation for the Compressive Data Gathering (CDG) in Wireless Sensor Network (WSN), a systematic introduction to the related research results at home and abroad so far was made. Firstly, the establishment of the frameworks of CDG and improved methods was introduced. Secondly, according to the transmission modes of WSN and Compressed Sensing (CS) theory respectively, the various methods of CDG were elaborated by classification. Then the problems of adaptation and optimization of CDG, the application of CS combined with other methods, and some examples of practical application were illustrated. Finally, the disadvantages in CDG and the development directions of CDG were pointed out.

To select key users with great information dissemination capability efficiently and effectively from large-scale social networks and corresponding historical user massages, an approach for selecting key users was proposed. Firstly, the structure information of the social network was used to construct the directed graph with the user as the node. Based on the Spark calculation framework, the weights of user activity, transmission interaction and information quantity were quantitatively calculated by the historical data of the message, so as to construct a dynamic weighted graph model of social networks. Then, the measurement for user's information dissemination capacity was established based on PageRank and the Spark-based algorithm was given correspondingly for large-scale social networks. Further more, the algorithm for d-distance selection of key users was given to make the overlap of information dissemination ranges of different key users be as less as possible by multiple iterations. The experimental results based on Sina Weibo datasets show that the proposed approach is efficient, feasible and scalable, and can provide underlying techniques to control the spread of bad news and monitor public opinions to a certain extent.

Resource allocation strategy has been a hot and difficult research topic in cloud computing field. In view of the fair distribution of multi-dimensional resources in heterogeneous cloud computing environment, two resource allocation strategies were proposed by combining Genetic Algorithm (GA) and Different Evolution (DE) algorithm and taking into account both fairness and efficiency in heterogeneous cloud environment. The solution matrix was improved to convert the Dominant Resource Fairness allocation in Heterogeneous systems (DRFH) model into Integer Linear Programming (ILP) model, a Max Task Match (MTM) based algorithm was used to generate initial solutions, and a revising operation was brought to change infeasible solutions into feasible solutions, which can accelerate the convergence to acquire the optimal solution quickly and effectively. Experimental results demonstrate that the multi-dimensional resources fair allocation strategies based on GA and DE algorithm can obtain near-optimal solutions; and in aspects of maximizing the value of minimum global dominant share and resource utilization, it is superior to Best-Fit DRFH and Distributed-DRFH, and has higher environmental adaptability to the resource requirement of different task types.

A joint time-frequency audio feature extraction algorithm based on Gabor dictionary and weight tensor of sparse representation was proposed to describe the characteristic of non-stationary audio signal. Conventional sparse representation uses a predefined dictionary to encode the audio signal as sparse weight vector. In this paper, the elements in the weight vector were reorganized into tensor format. Each order of the tensor respectively characterized time, frequency and duration property of signal, making it the joint time-frequency-duration representation of the signal. The frequency factors and duration factors were concatenated as audio features through tensor decomposition. To solve the over-fitting problem of sparse tensor factorization, an automatic-adjust-penalty-coefficient factorization algorithm was proposed. The experimental results show that the proposed feature outperforms MFCC (Mel-Frequency Cepstrum Coefficient) feature, MFCC+MP feature concatenated by MFCC and Matching Pursuit (MP) features, and nonuniform scale-frequency map feature by 28.0%, 19.8% and 6.7% respectively, in 15-category audio classification.

The current study of instance-transfer learning suffers from the mismatch between the granularities of data from multi-source heterogeneous domains. A Transfer Sparse unsupervised Hierarchical Probabilistic Self-Organizing Graph (TSHiPSOG) method based on the framework of Hierarchical Probabilistic Self-Organizing Graph (HiPSOG) method in the single domain was proposed. Firstly, representation vectors with different granularities were extracted from source and target domains by using hierarchical self-organizing model based on a probabilistic mixture of multivariate Gaussian component; and the sparse graph probabilistic criterion was used to control the growth of the model. Secondly, the most similar representation vector of the target domain data was searched in the rich-information source domain by using the Maximum Information Coefficient (MIC). Then, the data in the target domain was classified using labels of similar representation vectors in the source domain. Finally, the experimental results on the international universal 20 Newsgroups dataset and the spam detection dataset show that the proposed method improves the average classifying accuracy of target domain using the information from source domain by 15.26% and 9.05%. Moreover, the approach improves the average classifying accuracy with mining different granularity representation vectors by 4.48% and 4.13%.

Images of transmission tower acquired by Unmanned Aerial Vehicle (UAV) have high resolution and complex background, the traditional stitching algorithm using feature points can detect a large number of feature points from background which costs much time and affects the matching accuracy. For solving this problem, a new image mosaic algorithm with quick speed and strong robustness was proposed. To reduce the influence of the background, each image was first segmented into foreground and background based on a new implementation method of salient region detection. To improve the feature point extraction and reduce the computation complexity, transformation matrix was calculated and image registration was completed by ORB (Oriented Features from Accelerated Segment Test (FAST) and Rotated Binary Robust Independent Elementary Features (BRIEF)) feature. Finally, the image mosaic was realized with image fusion method based on multi-scale analysis. The experimental results indicate that the proposed algorithm can complete image mosaic precisely and quickly with satisfactory mosaic effect.

Many existing image classification algorithms cannot be used for big image data. A new approach was proposed to accelerate big image classification based on MapReduce. The whole image classification process was reconstructed to fit the MapReduce programming model. First, the Scale Invariant Feature Transform (SIFT) feature was extracted by MapReduce, then it was converted to sparse vector using sparse coding to get the sparse feature of the image. The MapReduce was also used to distributed training of random forest, and on the basis of it, the big image classification was achieved parallel. The MapReduce based algorithm was evaluated on a Hadoop cluster. The experimental results show that the proposed approach can classify images simultaneously on Hadoop cluster with a good speedup rate.

In order to recommend trustworthy Web services, the differences between Web service recommendation and electronic commerce recommendation were analyzed, and then based on the collaborative filtering recommendation algorithm, a trustworthy Web service recommendation approach was proposed. At first, non-functional requirements of trustworthy software were evaluated. According to the evaluation results, similar users were filtered for the first time. Then, by using the rating information and basic information, the similar users were filtered for the second time. After finishing these two filtering procedures, the final recommendation users were determined. When using users' ratings information to calculate the similarity between the users, the similarity of the different services to the users was taken into consideration. When using users' basic information to calculate the similarity between the users, the Euclidean distance formula was introduced because of the nonlinear characteristics of the users. The problems of the dishonesty and insufficient number of users were also considered in the approach. At last, the experimental results show that the recommendation approach for trustworthy Web services is effective.

Concerning the existing problems in the current key management strategies, lower connectivity, higher storage consumption and communication cost, this paper proposed a mixed key management scheme based on domain for Wireless Sensor Network (WSN). The scheme divided the deployment area into a number of square areas, which consisted of member nodes and head nodes. According to their pre-distribution key space information, any pair of nodes in the same area could find a session key, but the nodes in different areas could only communicate with each other through head nodes. The eigenvalues and eigenvectors of the multiple asymmetric quadratic form polynomials were computed, and then the orthogonal diagonalization information was got, by which the head nodes could achieve identification and generate the session key between its neighbor nodes. The analysis of performance shows that compared with the existing key management schemes, this scheme has full connectivity and a bigger improvement in terms of communication overhead, storage consumption and safety.

Cloud computing is a new model of the Internet resource utilization to provide a variety of IT services. It has been widely used in various fields, including High Performance Computing (HPC). However, its virtualization has caused some performance overhead. Meanwhile, there are some differences in virtualization technology of different cloud platforms, so the performance in implementing HPC is different among them. The performance and real workload evaluation of HPC of open source clouds platforms, including Nimbus, OpenNebula and OpenStack, were compared and analyzed by HPC Challenge (HPCC) benchmark suite and NAS Parallel Benchmark (NPB) from CPU, memory, communication, scalability and HPC, respectively. The experimental results show that OpenStack has better performance for computation-intensive high performance applications, thus it is a good selection for implementing HPC applications in open source cloud platform.

The research on aggregate signcryption is mostly based on identity-based encryption to provide confidentiality and authentication, thus improving efficiency. But aggregate signcryption has the problem in certificate management and key escrow. Therefore, it needs to design new aggregate signcryption schemes, which not only solve the problem of certificate management and key escrow, but also guarantee the confidentiality and authentication of the scheme. This paper analyzed the main stream aggregate signcryption schemes and their development. Combined with the scheme of Zhang et al.(ZHANG L, ZHANG F T. A new certificateless aggregate signature scheme. Computer Communications, 2009,32(6)：1079-1085) and the needs mentioned above, this article designed a certificateless aggregate signcryption scheme, and proved its confidentiality and unforgeability based on the Bilinear Diffie-Hellman (BDH) problem and Computational Diffie-Hellman (CDH) problem. The experimental results show that the proposed scheme is more efficient and the amount of computation is equal or lower in comparison with the other schemes. What's more, the new scheme is publicly verifiable, and it eliminates the use of public key certificate and solves the problem in key escrow.

In order to effectively improve the image encryption effect and safety, based on studying image encryption algorithm based on chaotic systems and bit operations, an improved algorithm of digital image encryption algorithm was proposed. Firstly, Logistic map was used to generate chaotic sequences, constructing row and column vector to scramble pixel position by the proposed algorithm. Secondly, another piecewise nonlinear Logistic sequence was applied to construct gray scale scrambling amplification factor to scramble the image gray scale, meanwhile the two processes were iteratively done . The mentioned algorithm made not only the key space increase and gray histogram uniform, but also the pixel correlation weaker and the operation speed faster than traditional algorithms. The experimental results show that the improved algorithm has good encryption efficacy and safety.

In order to solve the bottleneck problems of memory capacity and CPU processing speed when the traditional K-Medoids clustering algorithm is used to deal with massive data, based on the in-depth study of K-Medoids algorithm, a parallel K-Medoids algorithm based on the MapReduce programming model was proposed. The part of Map function is to calculate the distance of each data object to the center point of the cluster and (re)allocation of their respective clusters, and the part of Reduce function is to calculate the new center point of each cluster according to the intermediate results of the Map section. The experimental results show that the parallel K-Medoids algorithm in the Hadoop cluster based on the MapReduce running has good clustering results and scalability, and for large data sets, the algorithm may get close to linear speedup.

To deal with the difficulties of lacking the discrimination method of network's traffic self-similarity and producing negative traffic based on classical Fractal Brown Motion (FBM), a discrimination method was proposed based on multiple order moment and a generation method was provided based on modified FBM model. Firstly, the mathematical formula of sample moment was studied. The discrimination method of self-similarity traffic was obtained on account of fractal moment analysis. Secondly, the classical Random Midpoint Displacement (RMD) algorithm was modified. At last, taking account of the real traffic of Bellcore and LBL, the discrimination method and generation method were given. The comparison of the simulation results with the actual experimental data proves that the method is feasible.

Energy efficiency is an important metric in wireless Ad Hoc networks. Until now, there is no universally accepted definition of energy efficiency, and the related results are asymptotic or qualitative, which has limited its applicability. By regarding a bit as a physical particle with one unit of mass, the authors assumed that a bit in transmission possessed a certain amount of kinetic information energy. As a result, the energy efficiency of wireless transmission was defined as the ratio of information energy to physical energy. A quantitative analysis on energy efficiency in wireless transmission was carried out and meaningful results were obtained. It is concluded that the energy efficiency changes non-monotonously with the transmission power, and there is an optimal transmission power, with which the maximum energy efficiency will be acquired. The optimal transmission power was given to help the protocol design. Based on the theoretical results, a practical solution for transmission power control was proposed, and an extensive experimental study of it was given on the CC2420 radio. The results show the effectiveness of the proposed transmission power control scheme.

Recent studies in Compressive Sensing (CS) have shown that sparse signals can be recovered from a small number of random measurements, which raises the question of whether random measurements can provide an efficient representation of sparse signals in an information-theoretic sense. To examine the influence of quantization errors, the average distortion introduced by quantizing compressive sensing measurements was studied using rate distortion theory. Both uniform quantization and non-uniform quantization were considered. The asymptotic rate-distortion functions were obtained when the signal was recovered from quantized measurements using different reconstruction algorithms. Both theoretical and experimental results shows that encoding a sparse signal through simple scalar quantization of random measurements incurs a significant penalty relative to direct or adaptive encoding of the sparse signal, but compressive sensing is able to exploit the sparsity to reduce the distortion, so quantized compressive sampling is suitable to be used to encode the low sparse signal.

Software security measurement is critical to the development of software and improvement of software security. Based on the entropy and attack surface proposed by Manadhata et al. (MANADHATA P K, TAN K M C, MAXION R A, et al. An approach to measuring a system's attack surface, CMU-CS-07-146. Pittsburgh: Carnegie Mellon University, 2007; MANADHATA P K, WING J M. An attack surface metric. IEEE Transactions on Software Engineering, 2011, 37(3): 371-386), a method of software security measurement was used to assess the threat of the software's resources and provide the threat weight of these resources. Based on the threat weight, the attack surface metric was calculated for determining whether a software product is secure in design, or in what aspect the software product can be improved. The method is demonstrated in a case to show that, when using the method, the probable security threats can be found as early as possible to prevent from producing the software products that may have vulnerabilities, and the directions for the improvement of software security are pointed out clearly.

To improve the performance of speaker recognition system, a new method of feature extraction was proposed based on Mel Frequency Cepstrum Coefficient (MFCC) and Inverted MFCC (IMFCC). This method constructed a mixed feature by combining MFCC with IMFCC using Fisher criterion. The experimental results show that the mixed feature proposed in this paper has better recognition performance compared with MFCC not only in the pure voice database but also in the noisy environments.

Concerning the disadvantage of the Minimalist Mutual-Authentication Protocol (M ²AP) that it cannot resist the man-in-the-middle attacks,an Enhanced M ²AP (EM ²AP) was presented by protecting the messages transmitted between reader and tag. The protocol calculated the Hamming weight of the shared key of tag and reader and then used the weight value to protect the transmitted message on the cyclic shift,thus effectively avoiding the man-in-the-middle attacks. By BAN logic, security analysis and performance analysis,this paper shows that this protocol cannot only maintain the low cost of tags,but also ensure good security and reliability.