A reasonable compromise of privacy and utility exists at an "appropriate" resolution of the data. We proposed novel mechanisms to achieve privacy preserving data publishing (PPDP) satisfying ε-differential privacy with improved utility through component analysis. The mechanisms studied in this article are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The differentia...

Privacy is a major concern in big data mining. Advances in development of data mining technologies bring serious risk to the security of individual’s sensitive data. An emerging research in data mining, known as privacypreserving data mining (PPDM), has been broadly studied in recent years. The basic idea of PPDM is to transform or change the data in such a way so as to not to compromise on sec...

Storage as a service has become an important paradigm in cloud computing for its great flexibility and economic savings. However, the development is hampered by data privacy concerns: data owners no longer physically possess the storage of their data. In this work, we study the issue of privacy-preserving set-valued data publishing. Existing data privacypreserving techniques (such as encryption...

Ling Guo. Randomization Based Privacy Preserving Categorical Data Analysis. Under the direction of Dr. Xintao Wu The success of data mining relies on the availability of high quality data. To ensure quality data mining, effective information sharing between organizations becomes a vital requirement in today’s society. Since data mining often involves sensitive information of individuals, the pu...

Social networks have become the universal consumer phenomena and have emerged with increasing popularity nowadays. The amount of network data grows enormously due to the increase of networking websites. The development of social networks has led to the increasing demand for the protection of privacy in publishing the social network data as the social network sites are accumulated with large amo...

Sequence data mining has many interesting applications in a large number of domains including finance, medicine, and business. However, Sequence data often contains sensitive information about individuals and improper release and usage of this data may lead to privacy violation. In this paper, we study the privacy issues in publishing multidimensional sequence data. We propose an anonymization ...