This paper proposes high speed kernel data collection method for analysis of memory workload, using technique of direct access to process`s memory management structure. The conventional analysis tools have a slower data collection speed and they are lack of scalability due to collection only formalized memory information. The proposed method collects kernel data much faster than the conventional methods using technique of direct collect to process`s memory information, page table, page structure in the memory management structure, and it can collect data which user wanted. We collect memory management data of the running process, and analyze its memory workload.

In this paper, we propose a distributed parent peer selection method to construct an efficient peer-to-peer(P2P) network topology by considering the capacity of a peer and the hop distance from a data source to the peer. To achieve this goal, we propose a method to combine the two performance metrics to calculate the probability that a peer becomes a parent peer. Using the probability, we propose a method to select a parent peer stochastically by making use of the state information of the neighboring peers that each peer maintains. Through simulation studies, we show that the proposed method drives high capacity peers to support more children peers and makes the diameter of the P2P network shorter than the other methods.

Several approaches to recommendation systems have been studied. One of the most successful technologies for building personalization and recommendation systems is collaborative filtering, which is a technique that provides a process of filtering customer information based on such information profiles. Collaborative filtering systems, however, have a sparsity if there is not enough data to recommend. In this paper, we suggest a movie recommendation system, based on the weighted personal propensity and the collaborating filtering system, in order to provide a solution to such sparsity. Furthermore, we assess the system`s applicability by using the open database MovieLens, and present a weighted personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the optimal personalization factors.

Users can access their data anywhere, at any time by using outsourced storage. But they cannot know how service provider manage the data. Even user cannot know when data damaged. To solve these problems, the outsourced storage auditing schemes has been proposed. Most proposed schemes are based on Homomorphic Verifiable Tags. But it has computational efficiency limitation because data used to exponent. In this paper, we propose a novel approach to outsourced storage auditing scheme using coefficient matrix. In the proposed scheme, data used to auditing by coefficient matrix form. Auditing procedures are proceed as solving the linear simultaneous equation. The auditor can audit easily by solving the equation using solution vector. The auditor can audit the n size data using sqrt(n) size data through out proposed scheme.

Personal information has been transmitted in a variety of services of Internet environment, but individual users do not know what information is sent. In this paper, we aim to develop a monitoring tool that continuously monitors personal sensitive information in network packets and informs the user whether or not to leak. So we implement a monitoring tool of personal information and analyze the experiment results. In addition, we introduce a prevention of confidential information in company and a leakage prevention of medical information, for applications that take direct advantage. The results of this study, by contributing to prevent leakage of personal information, can help reduce cyber threats variously targeting personal information of users.

In this paper, we present an anonymous authentication and location assurance protocol for secure location-aware services over vehicular ad hoc networks (VANETs). In other to achieve our goal, we propose the notion of a location-aware signing key so as to strongly bind geographic location information to cryptographic function while providing conditional privacy preservation which is a desirable property for secure vehicular communications. Furthermore, the proposed protocol provides an efficient procedure based on hash chain technique for revocation checking to effectively alleviate communication and computational costs on vehicles in VANETs. Finally, we demonstrate comprehensive analysis to confirm the fulfillment of the security objectives, and the efficiency and effectiveness of the proposed protocol.

Recently, the rapid development of network technology has enabled people to use various services on the internet. However, the existing password-based user authentication system used in the internet environment requires a password table, which is a potential security threat as it could be leaked by an insider. To solve this issue, remote user authentication methods that do not require a user password table have been proposed. Regarding remote user authentication using a smart card in particular, various methods have been suggested to reduce expenses and to improve stability and efficiency, but the possibility of impersonation attacks and password-guessing attacks using information saved in a user`s smart card still exist. Therefore, this study proposes a remote user authentication method that can safeguard against impersonation attacks and password guessing attacks, by analyzing weak points of conventional methods and creating a smart card`s ID and password that are based on the user`s ID and password.