Aiming at the problem that the existing differential privacy high-dimensional data publishing methods are difficult to take into account both the complex attribute correlation between data and computational cost, a differential privacy high-dimensional data publishing method based on clustering analysis technology, namely PrivBC, was proposed. Firstly, the attribute clustering method was designed based on the K-means++, the maximum information coefficient was introduced to quantify the correlation between the attributes, and the data attributes with high correlation were clustered. Secondly, for each data subset obtained by the clustering, the correlation matrix was calculated to reduce the candidate space of attribute pairs, and the Bayesian network satisfying differential privacy was constructed. Finally, each attribute was sampled according to the Bayesian networks, and a new private dataset was synthesized for publishing. Compared with PrivBayes method, PrivBC method had the misclassification rate and running time reduced by 12.6% and 30.2% averagely and respectively. Experimental results show that the proposed method can significantly improve the computational efficiency with ensuring the data availability, and provides a new idea for the private publishing of high-dimensional big data.

Cloud storage is popular to users for its high scalability, high reliability, and low-cost data management. However, it is an important security problem to safeguard the cloud data integrity. Currently, providing public auditing services based on semi-trusted third party is the most popular and effective cloud data integrity audit scheme, but there are still some shortcomings such as single point of failure, computing power bottlenecks, and low efficient positioning of erroneous data. Aiming at these defects, a dynamic cloud data audit model based on block chain was proposed. Firstly, distributed network and consensus algorithm were used to establish a block chain audit network with multiple audit entities to solve the problems of single point of failure and computing power bottlenecks. Then, on the guarantee of the reliability of block chain, chameleon Hash algorithm and nest Merkle Hash Tree (MHT) structure were introduced to realize the dynamic operation of cloud data tags in block chain. Finally, by using nest MHT structure and auxiliary path information, the efficiency of erroneous data positioning was increased when error occurring in audit procedure. The experimental results show that compared with the semi-trusted third-party cloud data dynamic audit scheme, the proposed model significantly improves the audit efficiency, reduces the data dynamic operation time cost and increases the erroneous data positioning efficiency.

The existing privacy protection technologies rarely consider query probability, map data, semantic information of Point of Information (POI) and other side information, so the attacker can deduce the privacy information of the user by combining the side information with the location data. To resolve this problem, a new algorithm was proposed to protect the location privacy of users, namely ARB (Anonymouse Region Building). Firstly, the space was divided into grids, and historical statistics were utilized to obtain the probability of queries for each grid of space. Then, the anonymous region for each user was obtained based on query probability of corresponding grid to protect the user's location privacy information. Finally, the location information entropy was used as a measure of privacy protection performance, and the performance of the proposed method was verified by comparison with the existing two methods on the real data set. The experimental results show that ARB obtains better privacy protection effect and lower computation complexity.

The data is usually shared by a group of users in cloud computing. The third party auditor can obtain the the identities of group members through their signatures of data blocks. In order to protect the identities of group members, the existing public audit schemes for shared data all hide the identities of group members. However, the anonymity of identity leads to the problem that a member of the group can change the shared data maliciously without being found, and the amount of computation is large for resource constrained devices in the process of generating signature for users. The existing public audit schemes have the problems that the identity of data block can not be traced and the amount of calculation of generating shared data block signature is large. In order to solve the above problems, A Scheme of sharing cloud Data Audit supporting user traceability and lightweight (ASDA) was proposed. Firstly, the security mediator was used to replace the user signature to protect the identities of group members. The information of user was saved while signing and it could be traced back that the data block was modified by which member through the above information, which could ensure the traceability of the identity of data block. Then, a new data block blinding technology was used to reduce the amount of client computing. The experimental results show that, compared with Storing shared Data on the cloud Via Security-mediator (SDVS) scheme, the proposed scheme reduces the computing time of users and realizes the traceability of shared data blocks.

Currently, most resource reallocation methods in cloud computing mainly aim to how to reduce active physical nodes for green computing, however, node stability of virtual machine placement solution is not considered. According to varying workload information of applications, a new virtual machine placement method based on multi-objective optimization was proposed for node stability, considering both the overhead of virtual machine reallocation and the stability of new virtual machine placement, and a new Multi-Objective optimization based Genetic Algorithm for Node Stability (MOGANS) was designed to solve this problem. The simulation results show that, the stability time of Virtual Machine (VM) placement obtained by MOGANS is 10.42 times as long as that of VM placement got by GA-NN (Genetic Algorithm for greeN computing and Numbers of migration). Meanwhile, MOGANS can well balance stability time and migration overhead.

In order to improve the training accuracy of radial basis neural network, this paper proposed a hybrid optimization algorithm. The algorithm used the strong global search ability of Particle Swarm Optimization (PSO) algorithm to avoid the adverse effect by choosing initial point in the K-means algorithm, thus improving the network center search speed. Meanwhile, the dynamic weight algorithm was used to avoid the ill-posed problem, and to further improve the network approximation ability. The boiler combustion instance indicates that the improved algorithm is efficient and practical.

Lithology identification is the most critical procedure in the logging data interpretation field, while the traditional lithology identification methods have a lot of defects such as slow explain efficiency, low accuracy, and big influenced human factors. To resolve these problems, a new kind lithology identification method was put forward using genetic optimized Radial Basis Probability Neural Network (RBPNN). Probabilistic Neural Network (PNN) and the Radial Basis Function Neural Network (RBFNN) were combined to construct RBPNN. To optimize network structure, upgrade convergence speed and accuracy, Genetic Algorithm (GA) was used to search for the optimal hidden center vector and matching kernel function control parameters of the RBPNN structure which must satisfy minimum error of RBPNN training and form genetic optimized RBPNN network model. The case study shows that lithology identification based on genetic optimized RBPNN can achieve the actual application standards, and it is feasible and effective, it also can provide scientific theoretical supports and dependences for oil geological exploration field.

Concerning the privacy disclosure in Social Networking Service (SNS), the usability of the privacy control in domestic SNS was studied. From the users' point of view, with the method of experiment and interview, usability testing and comparative analysis on the privacy control in Renren and Pengyou were handled. The result indicates that the privacy control in Pengyou is better than that in Renren, but there is no significant difference between the two sites. Both of them need some improvements. Renren needs to improve its centralized navigation of privacy control and the centralized privacy setting interface. Pengyou should improve its decentralized navigation of privacy control and the blacklist function.