Data mining technologies are popular for identifying interesting patterns and trends in large amounts of data. With the advent of high speed networks and easily available storage, many organizations are able to collect large amounts of data. On one hand, these organizations would like to mine their data to understand and discover interesting patterns; on the other hand, many legal and commercia...

Privacy-preserving data mining (PPDM) is a recent emergent research area that deals with the incorporation of privacy preserving concerns to data mining techniques. We consider a real clinical setting where the data is horizontally distributed among different institutions. Each one of the medical institutions involved in this work provides a database containing a subset of patients. There is re...

Privacy preserving data mining is a novel research direction in data mining and statistical databases, which has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. There have been two types of privacy proposed concerning data mining. The first type of privacy, called output privacy, is that the data is altered s...

With the rapid advance of the Internet, a large amount of sensitive data is collected, stored, and processed by different parties. Data mining is a powerful tool that can extract knowledge from large amounts of data. Generally, data mining requires that data be collected into a central site. However, privacy concerns may prevent different parties from sharing their data with others. Cryptograph...

Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need differen...

Time-stamped location information is regarded as spatio-temporal data due to its time and space dimensions and, by its nature, is highly vulnerable to misuse. Privacy issues related to collection, use and distribution of individuals’ location information are the main obstacles impeding knowledge discovery in spatio-temporal data. Suppressing identifiers from the data does not suffice since move...

Privacy preservation in data mining has gained significant recognition because of the increased concerns to ensure privacy of sensitive information. It enables multiple parties to conduct collaborative data mining while preserving the privacy of their data. In this work, a cloud computing based protocol for privacypreserving distributed K-means clustering over horizontally partitioned data, sha...

The privacy preserving data mining (PPDM) has been one of the most interesting, yet challenging, research issues. In the PPDM, we seek to outsource our data for data mining tasks to a third party while maintaining its privacy. In this paper, we revise one of the recent PPDM schemes (i.e., FS) which is designed for privacy preserving association rule mining (PP-ARM). Our analysis shows some limi...

Data mining is the task of discovering significant patterns/rules/results from a set of large amount of data stored in databases, data warehouse or in other information repositories. Even though the focus on datamining technology has been on the discovery of general patterns some data-mining applications may require to access individual’s records having sensitive privacy data. Abundance of reco...

Data mining technologies are popular for identifying interesting patterns and trends in large amounts of data. With the advent of high speed networks and easily available storage, many organizations are able to collect large amounts of data. On one hand, these organizations would like to mine their data to understand and discover interesting patterns; on the other hand, many legal and commercia...