Papers by Author: Jia Jin Le

Abstract: MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

Abstract: With the rapid development of computer technology, the demand for real-time information is becoming higher, many real-time web applications such as online booking system, stock trading system, instant messaging system, are supposed to send the constant changes of the server to the client in real time. On the basis of in-depth study of WebSocket protocol in HTML5 standard, this paper proposes a new real-time web application solution based on Node.js and WebSocket, aiming to significantly enhancing real-time performance, and more efficiently using the processing power of the server. Finally, the new solution is applied in the game You draw, I guess to theoretically and practically study the viability of the solution and the advantages compared to traditional solutions.

Abstract: Cloud computing seems to offer some incredible benefits for communicators: the availability of an incredible array of software applications, access to lightning-quick processing power, unlimited storage, and the ability to easily share and process information. All of this is available through your browser any time you can access the Internet. While this might all appear enticing, there remain issues of reliability, portability, privacy, and security. When our private data are out-sourced in cloud computing, we should guarantee the confidentiality and searchability of the private data. Our paper provides a new approach to avoid the disclosure of the sensitive attributes of users when user ask for service from the Service Provider (SP) in cloud computing.

Abstract: Some medical records are often added and deleted in the practical applications. The leakage of privacy information caused by re-publishing datasets with multiple sensitive attributes becomes more likely than any other publication styles. In this paper, we first systematically characterize the inference attacks and set the hierarchy sensitive attribute rules. Then we propose a novel privacy preserving model based on k-anonymity for re-publication of multiple sensitive datasets and verify the novel approach that can eliminate inference channel and effectively protect privacy information in re-publication of datasets with multiple sensitive attributes by specific example. Finally, we present the anonymization algorithm to achieve privacy against inference attack.