Study On The Technology Of Fast Information Encryption And Content Security Management In Electronic Commerce Environment

Along with rapid development of Electronic Commerce today, the study on the security technology in E-Commerce environment is absolutely necessary and significant.The security threat in E-Commerce environment is revieweded from the point of view of computer system level, E-Commerce application level and content security level. The security issue in the process of real application of E-Commerce is emphatically analyzed. It includes the fast information encryption issue base on the high real-time performance in the application of B2B E-Commerce model; the analysis of customer behavior in the E-Commerce environment which is concerned by enterprise in the application of B2C E-Commerce model; the other content security issues, such as the management of the malicious comment from web user which is concerned by the third-part carrier of E-Commerce applications platform, the anti-money laundering issue which is concerned by government. At the end, studying on the technology of fast information encryption and content security management in E-Commerce envirment is located as the study points.The attributions of the paper are:(1) An implementation method of fast modular multiplication in finite fields is proposed. With the requirement of high real-time performance and security during the information is exchanged in the application of B2B E-Commerce model, the hybrid encryption of AES and ECC usually is adopted for the application. In this solution, ECC is used to management the session key. When the real-time performance is especially emphasized, the process of real application in B2B E-Commerce model see the efficiency of ECC process is the key influencing factor. To improve the efficiency of ECC process, a faster modular multiplication in finite fields is requested. An improved algorithm base on the optimal normal basis (ONB) of type II is proposed in this paper. The proposed multiplier only requires (2m+1) cyclic shift operations,15m XOR gates and (m+1) AND gates to vectors.The results of stimulation with software and implementation on hardware show that the proposed method highly improves the modular multiplication efficiency compared with existing methods. The prospoed method was used in the process of real application of B2B E-Commerce model successfully;(2) A random walk method for sentiment classification (SCG) is presented. With the management of feedback information, especially those malicious comments, from web user in the E-Commerce environment, the technology of sentiment classification is very useful in many applications. In this paper, a novel method to tag words sentiment automatically is proposed. In this method, a word association graph is firstly constructed from text corpus, i.e. product reviews, in which each node is a word and if there is an edge between two words, it means the two words co-occur in the same sentence. And then, with a random walk algorithm, the sentiment score is calculated for all the words in the graph at one time. To show the effectiveness of our method, the sentiment tagging results are then used for sentiment classification on real data set. The experimental results show that the sentiment classification results with SCG are better than the compared methods, such ad SVM and SO-PMI;(3) A clustering method (E-ROCK) based on mixed data for customer behavior pattern discovering is presented. To deal with the issue of retaining customers and product recommendation in the application of B2C E-Commerce model, clustering is a reliable and efficient technology which used to discover customer behavior pattern and improve the personalization of E-Commerce systems. However, current research on clustering algorithm usually based on numeric data or categorical data, and is not suitable for mixed data set which including both numeric data and categorical data, such as the user ID, access time, the customer visited pages'URL, record of trades, commodity type, consumption etc..According the analysis of those current mainstream clustering methods, ROCK is choosed as the prototype algorithm in the research. As ROCK is only suitable for handling categorical data, to analysis customer behavior, mixed data set must be handled. With extending the ROCK algorithm, a novel method (E-ROCK) to deal with mixed data set is proposed in this paper. Experiment with real application data shows the E-ROCK algorithm is efficient and successful. At the end, the framework of a real existing B2C E-Commerce platform is introduced. The platform include two background sub-systems, feedback information analysis system and product recommendation system, where the SCG algorithm and E-ROCK algorithm are applied.