Privacy Issues In Data Mining

Data mining is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

B.2 Introduction

The growing popularity and development of data mining technologies bring serious threat to the security of individual's

We identified four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of

Although data mining can be extremely valuable in many applications (e.g. business, medical analysis, etcetera) it can also, in the absence of adequate safeguards, violate informational privacy. Privacy can be violated if personal data are used for other purposes subsequent to the original transaction between an individual and an organization when the information was collected. During the whole process of data mining (gathering of data to discovery of knowledge) these data, which generally contain sensitive individual information such as medical and financial information, often get exposed to collectors, owners, users and miners. The huge amount of data available means that it is possible to learn a lot of information about individuals from public data. Privacy preserving has originated as an important concern with reference to the success of the data mining. Privacy preserving data mining (PPDM) deals with protecting the privacy of individual data or sensitive knowledge without sacrificing the utility of the data.

Medical Databases:

The Scrub system was designed for de-identification of clinical notes and letters which typically occurs in the form of textual data. Clinical notes and letters are typically in the form of text which contain references to patients, family members, addresses, phone numbers or providers. Traditional techniques simply use a global search and replace procedure in order