This paper presents an efficient protocol for securely computing the fundamental problem of pattern matching. This problem is defined in the two-party setting, where party P1 hold...
Rosario Gennaro, Carmit Hazay, Jeffrey S. Sorensen
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...