Protecting Medical Data for Decision-Making Analyses

Abstract

In this paper, we present a procedure for data protection, which can be applied before any model building based analyses are performed. In medical environments, abundant data exist, but because of the lack of knowledge, they are rarely analyzed, although they hide valuable and often life-saving knowledge. To be able to analyze the data, the analyst needs to have a full access to the relevant sources, but this may be in the direct contradiction with the demand that data remain secure, and more importantly in medical area, private. This is especially the case if the data analyst is outsourced and not directly affiliated with the data owner. We address this issue and propose a solution where the model-building process is still possible while data are better protected. We consider the case where the distributions of original data values are preserved while the values themselves change, so that the resulting model is equivalent to the one built with original data.

Brute force analysis of PsychoPass-generated Passwords

“…One can only increase the time required. Thus let us assume a typical 60-year time span of usefulness of stored information [40][41][42][43][44]. In other words, a safe password today (in 2014) would be made of at least nine characters from upper and lowercase letters, numbers and symbols, but it would not be safe in 10 years from now.…”

Privacy Preserving in Association Rules Mining with VPA Algorithm

“…The basic idea of secure multiparty computation is that a computation is secure if at the end of the computation, no party knows anything except its own input and the results, which is used in the field of PPDM. Linfton et al proposed distributed data mining, in which secure multiparty computation was used to build the secure set union and preserve the privacy [6,7]. In terms of data mining, most of the way of preserving privacy are based on Clustering, Bayesian classification and decision tree.…”