Preventing Data Leakage in a Cloud Environment

Despite the multifaceted advantages of cloud computing,concerns about data leakage or abuse impedes its adoption for security-sensi tive tasks.Recent investigations have revealed that the risk of unauthorized data access is one of the biggest concerns of users of cloud-based services.Transparency and accountability for data managed in the cloud is necessary.Specifically,when using a cloudhost service,a user typically has to trust both the cloud service provider and cloud infrastructure provider to properly handling private data.This is a multi-party system.Three particular trust models can be used according to the credibility of these providers.This pa per describes techniques for preventing data leakage that can be used with these different models.

A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network

It is of vital importance to reduce injuries and economic losses by accurate forecasts of typhoon tracks. A huge amount of typhoon observations have been accumulated by the meteorological department, however, they are yet to be adequately utilized. It is an effective method to employ machine learning to perform forecasts. A long short term memory（LSTM） neural network is trained based on the typhoon observations during 1949–2011 in China＇s Mainland, combined with big data and data mining technologies, and a forecast model based on machine learning for the prediction of typhoon tracks is developed. The results show that the employed algorithm produces desirable 6–24 h nowcasting of typhoon tracks with an improved precision.

Comparison between several satellite constellation schemes for MEO-TDRSS of China

The satellite constellation classes, which are suitable for the medium earth orbit tracking and data relay satellite system （MEO-TDRSS） of China, are investigated. On the basis of the functionality and the traffic distribution characteristic of MEO-TDRSS, the coverage performance and inter-satellite link properties of four different constellation schemes are compared by simulations. Simulation results indicate that the rosette and common-track constellations, whose satellites are distributed on the celestial sphere more uniformly, are appropriate for the implementation of MEO-TDRSS of China.

Why Did the Reform Fail?The Impact of the Personnel System Reform on Teachers in Jia County of China

Over recent years,the reform-driven development of education has been one primary idea of the Chinese government under the leadership of Xi Jinping.Above all,the reform of the personnel system,closely related to the interests of teachers,becomes the most sensitive link in the educational reform.To root out the restriction of education system on teachers and enhance their enthusiasm for work,Jia County carried out a reform of teacher personnel system from 2013 to 2016,including breaking the hierarchical restriction on teachers’administrative promotion,decentralizing the power of confirming the teachers’professional titles,eliminating the teachers with the worst performances,practicing performance salary,and other measures.In this study,we tracked 627 teachers regarding their work enthusiasm and stress during the three-year reform,analyzed the data through the Hierarchical Linear Model(HLM),and found that:(1)The teachers’enthusiasm for work increased over time,but did not significantly change with the consideration of the influence of the principals’leadership in teaching.The personnel system reform aiming at motivating teachers failed to work as expected.(2)The teachers’work stress still increased year by year.Therefore,this study concludes that the reform in Jia County failed to fulfill its goals and that the vigorous top-down reform did not lead to ideal results.A reason might be that the reform damaged the vested interests of teachers who represent the deep-rooted nepotism typically in the Chinese education community.Reforms of educational personnel system should give schools a full play regarding educational governance in the future.

Software Backdoor Analysis Based on Sensitive Flow Tracking and Concolic Execution

In order to effectively detect and analyze the backdoors this paper introduces a method named Backdoor Analysis based on Sensitive flow tracking and Concolic Execution（BASEC）.BASEC uses sensitive flow tracking to effectively discover backdoor behaviors, such as stealing secret information and injecting evil data into system, with less false negatives. With concolic execution on predetermined path, the backdoor trigger condition can be extracted and analyzed to achieve high accuracy. BASEC has been implemented and experimented on several software backdoor samples widespread on the Internet, and over 90% of them can be detected. Compared with behavior-based and system-call-based detection methods, BASEC relies less on the historical sample collections, and is more effective in detecting software backdoors, especially those injected into software by modifying and recompiling source codes.