Addressing data privacy in matched studies via virtual pooling

Addressing data privacy in matched studies via virtual pooling

BACKGROUND Data confidentiality and shared use of research data are two desirable but sometimes conflicting goals in research with multi-center studies and distributed data. While ideal for straightforward analysis, confidentiality restrictions forbid creation of a single dataset that includes covariate information of all participants. Current approaches such as aggregate data sharing, distribu...

Addressing the Dilema Between Collaboration and Privacy in Coworking Spaces

This paper aims to inform design strategies for regulating privacy in coworking spaces. Coworking spaces are growing at a high rate, yet studies related to the social, psychological, behavioral and physical needs associated with these environments are limited. The growth of coworking spaces is in greater part facilitated by a drive towards greater interaction and collaboration among the workfor...

On Addressing Efficiency Concerns in Privacy Preserving Data Mining

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. To encourage users to provide correct inputs, we recently proposed a data distortion scheme for association rule mining that simultaneously provides both privacy to the user and accuracy in the mining results. However, mining the distorte...

Matched-Pair Studies with Misclassified Ordinal Data

Privacy and Security of Big Data in THE Cloud

Big data has been arising a growing interest in both scien- tific and industrial fields for its potential value. However, before employing big data technology into massive appli- cations, a basic but also principle topic should be investigated: security and privacy. One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. Many or...