Recently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many pri...

Privacy is an important issue in Data mining. The privacy field has seen speedy advances in current years because ability to store data has increased. Precisely, current advances in the data mining field have led to increased concerns about privacy. Privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. . Many met...

Privacy preserving data mining – getting valid data mining results without learning the underlying data values – has been receiving attention in the research community and beyond. It is unclear what privacy preserving means. This paper provides a framework and metrics for discussing the meaning of privacy preserving data mining, as a foundation for further research in this field.

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed pr...

Through data mining collect large amount of data in many organizations. A key value of huge databases today is technical or financial research. In a huge collection of data there arises a key issue that is privacy. Due to personal interests, medical databases or business interests privacy is needed. Due to privacy infringement while performing the data mining operations this is often not possib...

In various distributed data mining settings, leakage of the real data is not adequate because of privacy issues. To overcome this problem, numerous privacy-preserving distributed data mining practices have been suggested such as protect privacy of their data by perturbing it with a randomization algorithm and using cryptographic techniques. In this paper, we review and provide extensive survey ...

Recent interest in data collection and monitoring using data mining for security and business-related applications has raised privacy. Privacy Preserving Data Mining (PPDM) techniques require data modification to disinfect them from sensitive information or to anonymize them at an uncertainty level. This study uses PPDM with adult dataset to investigate effects of K-anonymization for evaluation...

What is Privacy Preserving Data Mining is the process of hiding and protecting sensitive data of individuals. In the recent era, we use many applications which require personal sensitive data of individuals. Thus, people are more concern about sharing their personal sensitive information due to increase of privacy intrusions. Since last two decades many Privacy Preserving Data Mining techniques...

The problem of secure distributed classification is an important one. In many situations, data is split between multiple organizations. These organizations may want to utilize all of the data to create more accurate predictive models while revealing neither their training data / databases nor the instances to be classified. The Naive Bayes Classifier is a simple but efficient baseline classifie...