The increasing use of data-mining tools in both the public and private sectors raises
concerns regarding the potentially sensitive nature of much of the data being mined. The …

Privacy-preserving data mining has concentrated on obtaining valid results when the input
data is private. An extreme example is Secure Multiparty Computation-based methods …

In this paper we introduce the concept of privacy preserving data mining. In our model, two
parties owning confidential databases wish to run a data mining algorithm on the union of …

We provide here an overview of the new and rapidly emerging research area of privacy
preserving data mining. We also propose a classification hierarchy that sets the basis for …

Although successful in many applications, data mining poses special concerns for private
data. An integrated architecture takes a systemic view of the problem, implementing …

Privacy preserving mining of distributed data has numerous applications. Each application
poses different constraints: What is meant by privacy, what are the desired results, how is the …

Data mining is under attack from privacy advocates because of a misunderstanding about
what it actually is and a valid concern about how it is generally done. This article shows how …

Privacy preserving data mining–getting valid data mining results without learning the
underlying data values–has been receiving attention in the research community and …

In this paper we address the issue of privacy preserving data mining. Specifically, we
consider a scenario in which two parties owning confidential databases wish to run a data …

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 …