Biblio

The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.

Cloud computing is widely deployed to handle challenges such as big data processing and storage. Due to the outsourcing and sharing feature of cloud computing, security is one of the main concerns that hinders the end users to shift their businesses to the cloud. A lot of cryptographic techniques have been proposed to alleviate the data security issues in cloud computing, but most of these works focus on solving a specific security problem such as data sharing, comparison, searching, etc. At the same time, little efforts have been done on program security and formalization of the security requirements in the context of cloud computing. We propose a formal definition of the security of cloud computing, which captures the essence of the security requirements of both data and program. Analysis of some existing technologies under the proposed definition shows the effectiveness of the definition. We also give a simple look-up table based solution for secure cloud computing which satisfies the given definition. As FPGA uses look-up table as its main computation component, it is a suitable hardware platform for the proposed secure cloud computing scheme. So we use FPGAs to implement the proposed solution for k-means clustering algorithm, which shows the effectiveness of the proposed solution.