Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. We find current techniques to preserve privacy in data mining are not effective in preserving time-domain privacy. We present t...

Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition of time dimension. We find current techniques to preserve privacy in data mining is not effective in preserving time-domain privacy. We present da...

Privacy has become an important issue in the progress of data mining techniques. Many laws are being enacted in various countries to protect the privacy of data. This privacy concern has been addressed by developing data mining techniques under a framework called privacy preserving data mining. Presently there are two main approaches popularly used -data perturbation and secure multiparty compu...

Privacy protection is indispensable in data mining, and many privacy-preserving data mining (PPDM) methods have been proposed. One such method is based on singular value decomposition (SVD), which uses SVD to find unimportant information for data mining and removes it to protect privacy. Independent component analysis (ICA) is another data analysis method. If both SVD and ICA are used, unimport...

Kantarcıoğlu, Murat. Ph.D., Purdue University, August, 2005. Privacy-Preserving Distributed Data Mining and Processing on Horizontally Partitioned Data. Major Professor: Christopher W. Clifton. Data mining can extract important knowledge from large data collections, but sometimes these collections are split among various parties. Data warehousing, bringing data from multiple sources under a sin...

Often, the information is sensitive or private in nature and these sensitive data when mined violates the privacy of the individuals. Privacy preserving data mining (PPDM) mines the data but intends to preserve the privacy of susceptible data without ever actually seeing it. This paper recaps the important techniques in PPDM like anonymization, perturbation and cryptography. Nowadays, data mini...

Research has been conducted on distributed data mining, privacy preserving data mining, and multiagent systems; however, integrating the aforementioned research areas require further investigation. Sensitive data sources, such as healthcare organizations, may be reluctant to permit sensitive data to leave the source or permit mining of the sensitive data. Additionally, distributed data mining o...

Privacy-Preserving Data Mining – developing models without seeing the data – is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Näıve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This...

Privacy-Preserving Data Mining – developing models without seeing the data – is receiving growing attention. This paper assumes a privacy-preserving distributed data mining scenario: data sources collaborate to develop a global model, but must not disclose their data to others. Näıve Bayes is often used as a baseline classifier, consistently providing reasonable classification performance. This...