Privacy and security concerns can prevent sharing of data, derailing data mining projects.
Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to …

This paper explores the possibility of using multiplicative random projection matrices for
privacy preserving distributed data mining. It specifically considers the problem of computing …

Classical data mining algorithms implicitly assume complete access to all data, either in
centralized or federated form. However, privacy and security concerns often prevent sharing …

Data mining has emerged as a significant technology for gaining knowledge from vast
quantities of data. However, there has been growing concern that use of this technology is …

There has been concern over the apparent conflict between privacy and data mining. There
is no inherent conflict, as most types of data mining produce summary results that do not …

Privacy-preserving data mining—developing models without seeing the data–is receiving
growing attention. This paper assumes a privacy-preserving distributed data mining …

Traditional Data Mining and Knowledge Discovery algorithms assume free access to data,
either at a centralized location or in federated form. Increasingly, privacy and security …

Abstract Privacy-Preserving Data Mining–developing models without seeing the data–is
receiving growing attention. This paper assumes a privacy-preserving distributed data …

Abstract Traditional Data Mining and Knowledge Discovery algorithms assume free access
to data, either at a centralized location or in federated form. Increasingly, privacy and …

Privacy and security concerns can prevent sharing of data, derailing data-mining projects.
Distributed knowledge discovery, if done correctly, can alleviate this problem. We introduce …