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OGISO, S., Mohri, M., Shiraishi, Y..  2020.  Transparent Provable Data Possession Scheme for Cloud Storage. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1–5.
Provable Data Possession (PDP) is one of the data security techniques to make sure that the data stored in the cloud storage exists. In PDP, the integrity of the data stored in the cloud storage is probabilistically verified by the user or a third-party auditor. In the conventional PDP, the user creates the metadata used for audition. From the viewpoint of user convenience, it is desirable to be able to audit without operations other than uploading. In other words, the challenge is to provide a transparent PDP that verifies the integrity of files according to the general cloud storage system model so as not to add operations to users. We propose a scheme in which the cloud generates the metadata used during verification, and the user only uploads files. It is shown that the proposed scheme is resistant to the forgery of cloud proof and the acquisition of data by a third-party auditor.
Hirotomo, M., Nishio, Y., Kamizono, M., Fukuta, Y., Mohri, M., Shiraishi, Y..  2017.  Efficient Method for Analyzing Malicious Websites by Using Multi-Environment Analysis System. 2017 12th Asia Joint Conference on Information Security (AsiaJCIS). :48–54.
The malicious websites used by drive-by download attacks change their behavior for web client environments. To analyze the behavior of malicious websites, the single-environment analysis cannot obtain sufficient information. Hence, it is difficult to analyze the whole aspect of malicious websites. Also, the code obfuscation and cloaking are used in malicious websites to avoid to be analyzed their behavior. In this paper, we propose an analyzing method that combines decoding of the obfuscation code with dynamic analysis using multi-environment analysis system in order to analyze the behavior of the malicious websites in detail. Furthermore, we present two approaches to improve the multi-environment analysis. The first one is automation of traffic log analysis to reduce the cost of analyzing huge traffic logs between the environments and malicious websites. The second one is multimodal analysis for finding the URL of malicious websites.