In the cloud computing environment, outsourcing service mode of data storage causes the security problem, the reliability of data cannot be guaranteed, and the privacy preservation problem has aroused wide concern. In...In the cloud computing environment, outsourcing service mode of data storage causes the security problem, the reliability of data cannot be guaranteed, and the privacy preservation problem has aroused wide concern. In order to solve the problem of inefficiency and high-complexity caused by traditional privacy preservation methods such as data encryption and access control technology, a privacy preservation method based on data coloring is proposed. The data coloring model is established and the coloring mechanism is adopted to deal with the sensitive data of numerical attributes, and the cloud model similarity measurement based on arithmetic average least-approximability is adopted to authenticate the ownership of privacy data. On the premise of high availability of data, the method strengthens the security of the privacy information. Then, the performance, validity and the parameter errors of the algorithm are quantitatively analyzed by the experiments using the UCI dataset. Under the same conditions of privacy preservation requirements, the proposed method can track privacy leakage efficiently and reduce privacy leakage risks. Compared with the k-anonymity approach, the proposed method enhances the computational time efficiency by 18.5%.展开更多
When cause of the aliasing lack probl using borehole sensors and microseimic events to image, spatial aliasing often occurred be- of sensors underground and the distance between the sensors which were too large. To so...When cause of the aliasing lack probl using borehole sensors and microseimic events to image, spatial aliasing often occurred be- of sensors underground and the distance between the sensors which were too large. To solve em, data reconstruction is often needed. Curvelet transform sparsity constrained inversion was widely used in the seismic data reconstruction field for its anisotropic, muhiscale and local basis. However, for the downhole ease, because the number of sampling point is mueh larger than the number of the sensors, the advantage of the cnrvelet basis can＇t perform very well. To mitigate the problem, the method that joints spline and curvlet-based compressive sensing was proposed. First, we applied the spline interpolation to the first arri- vals that to be interpolated. And the events are moved to a certain direction, such as horizontal, which can be represented by the curvelet basis sparsely. Under the spasity condition, curvelet-based compressive sensing was applied for the data, and directional filter was also used to mute the near vertical noises. After that, the events were shifted to the spline line to finish the interpolation workflow. The method was applied to a synthetic mod- el, and better result was presented than using curvelet transform interpolation directly. We applied the method to a real dataset, a mieroseismic downhole observation field data in Nanyang, using Kirchhoff migration method to image the microseimic event. Compared with the origin data, artifacts were suppressed on a certain degree.展开更多
This study evaluates the operational performance of all routes of Sajha Bus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis (DEA) in terms of efficiency and effectiveness score. This approach...This study evaluates the operational performance of all routes of Sajha Bus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis (DEA) in terms of efficiency and effectiveness score. This approach allows us to access the relative performance of transit system in absence of historical data and research to compare with. To explore the possibility of enhancing the performance, scenarios were created for relatively underperforming routes and long route problem by changing the most important input variable and output variables accordingly with regression model where it was relevant. Partial Least Squares (PLS) regression was used to determine the most influential input variables to the output variables. DEA was conducted to access the performance of all routes under these scenarios. Underperforming routes except the longest route under the first set of scenarios, emerge to be better performing efficiently without considerable negative deviation in effectiveness. The result of second set of scenarios for long route problem suggests that the longest route’s performance can be enhanced significantly upon proper route alignment. Scenarios development and evaluation can help lead transit companies to explore the strategies to facilitate operational performance enhancement.展开更多
Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the...Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61272458Shaanxi Provinces Natural Science Basic Research Planning Project under Grant No.2014JM2-6119Yu Lin Industry-Academy-Research Cooperation Project under Grant No.2014CXY-12
文摘In the cloud computing environment, outsourcing service mode of data storage causes the security problem, the reliability of data cannot be guaranteed, and the privacy preservation problem has aroused wide concern. In order to solve the problem of inefficiency and high-complexity caused by traditional privacy preservation methods such as data encryption and access control technology, a privacy preservation method based on data coloring is proposed. The data coloring model is established and the coloring mechanism is adopted to deal with the sensitive data of numerical attributes, and the cloud model similarity measurement based on arithmetic average least-approximability is adopted to authenticate the ownership of privacy data. On the premise of high availability of data, the method strengthens the security of the privacy information. Then, the performance, validity and the parameter errors of the algorithm are quantitatively analyzed by the experiments using the UCI dataset. Under the same conditions of privacy preservation requirements, the proposed method can track privacy leakage efficiently and reduce privacy leakage risks. Compared with the k-anonymity approach, the proposed method enhances the computational time efficiency by 18.5%.
基金Supported by Project of the National Natural Science Foundation of China(No.41274055)
文摘When cause of the aliasing lack probl using borehole sensors and microseimic events to image, spatial aliasing often occurred be- of sensors underground and the distance between the sensors which were too large. To solve em, data reconstruction is often needed. Curvelet transform sparsity constrained inversion was widely used in the seismic data reconstruction field for its anisotropic, muhiscale and local basis. However, for the downhole ease, because the number of sampling point is mueh larger than the number of the sensors, the advantage of the cnrvelet basis can＇t perform very well. To mitigate the problem, the method that joints spline and curvlet-based compressive sensing was proposed. First, we applied the spline interpolation to the first arri- vals that to be interpolated. And the events are moved to a certain direction, such as horizontal, which can be represented by the curvelet basis sparsely. Under the spasity condition, curvelet-based compressive sensing was applied for the data, and directional filter was also used to mute the near vertical noises. After that, the events were shifted to the spline line to finish the interpolation workflow. The method was applied to a synthetic mod- el, and better result was presented than using curvelet transform interpolation directly. We applied the method to a real dataset, a mieroseismic downhole observation field data in Nanyang, using Kirchhoff migration method to image the microseimic event. Compared with the origin data, artifacts were suppressed on a certain degree.
文摘This study evaluates the operational performance of all routes of Sajha Bus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis (DEA) in terms of efficiency and effectiveness score. This approach allows us to access the relative performance of transit system in absence of historical data and research to compare with. To explore the possibility of enhancing the performance, scenarios were created for relatively underperforming routes and long route problem by changing the most important input variable and output variables accordingly with regression model where it was relevant. Partial Least Squares (PLS) regression was used to determine the most influential input variables to the output variables. DEA was conducted to access the performance of all routes under these scenarios. Underperforming routes except the longest route under the first set of scenarios, emerge to be better performing efficiently without considerable negative deviation in effectiveness. The result of second set of scenarios for long route problem suggests that the longest route’s performance can be enhanced significantly upon proper route alignment. Scenarios development and evaluation can help lead transit companies to explore the strategies to facilitate operational performance enhancement.
文摘Cyber losses in terms of number of records breached under cyber incidents commonly feature a significant portion of zeros, specific characteristics of mid-range losses and large losses, which make it hard to model the whole range of the losses using a standard loss distribution. We tackle this modeling problem by proposing a three-component spliced regression model that can simultaneously model zeros, moderate and large losses and consider heterogeneous effects in mixture components. To apply our proposed model to Privacy Right Clearinghouse (PRC) data breach chronology, we segment geographical groups using unsupervised cluster analysis, and utilize a covariate-dependent probability to model zero losses, finite mixture distributions for moderate body and an extreme value distribution for large losses capturing the heavy-tailed nature of the loss data. Parameters and coefficients are estimated using the Expectation-Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.
