We present an efficient protocol for the privacy-preserving, distributed learning of decision-tree classifiers. Our protocol allows a user to construct a classifier on a database h...
Many databases will not or can not be disclosed without strong guarantees that no sensitive information can be extracted. To address this concern several data perturbation techniq...
In this paper, we focus our attention on the problem of computing the ratio of two numbers, both of which are the summations of the private numbers distributed in different parties...
There are many applications for Secure Multi-Party Computation (SMC), but practical adoption is still an issue. One reason is that the business model of the application does not m...