In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. A recent framework performs privacy preserving data mining by using a condensation based approach....

Secure multiparty computation allows multiple parties to participate in a computation. SMC (secure multiparty computation) assumes n parties where n>1. All the parties jointly compute a function. Privacy preserving data mining has become an emerging field in the secure multiparty computation. Privacy preserving data mining preserves the privacy of individual's data. Privacy preserving data mini...

Various applications following the Internet of Things (IoT) paradigm have become a part of our everyday lives. Therefore, designing mechanisms for security, trust and privacy for this context is important. As one example, applications related to electronic surveillance and monitoring have serious issues related to privacy. Research is needed on how to design privacy preserving surveillance syst...

Nowadays, publishing data publically is an important for many purposes especially for scientific research. Publishing this data in its raw form make it vulnerable to privacy attacks. Therefore, there is a need to apply suitable privacy preserving techniques on the published data. K-anonymity and L-diversity are well known techniques for data privacy preserving. These techniques cannot face the ...

Privacy is a major issue today as more and more users are connecting and participating in the Internet. This paper discusses privacy issues associated with web metering schemes and explores the dilemma of convincing interested parties of the merits of web metering results with sufficient detail, and still preserving users’ privacy. We analyse different categories of web metering schemes using a...

Title of Dissertation: Multiplicative Data Perturbation for Privacy Preserving Data Mining Kun Liu, Doctor of Philosophy, 2007 Dissertation directed by: Dr. Hillol Kargupta Associate Professor Department of Computer Science and Electrical Engineering Recent interest in the collection and monitoring of data using data mining technology for the purpose of security and business-related application...

We consider privacy preserving decision tree induction via ID3 in the case where the training data is horizontally or vertically distributed. Furthermore, we consider the same problem in the case where the data is both horizontally and vertically distributed, a situation we refer to as grid partitioned data. We give an algorithm for privacy preserving ID3 over horizontally partitioned data invo...

We consider scenarios in which two parties, each in possession of a graph, wish to compute some algorithm on their joint graph in a privacy-preserving manner, that is, without leaking any information about their inputs except that revealed by the algorithm’s output. Working in the standard secure multi-party computation paradigm, we present new algorithms for privacy-preserving computation of A...

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...