Trust Enabled Secure Multiparty Computation

Abstract

Hamiltonian cycles play an important role in graph theory and data mining applications. Two Hamiltonian cycles that don't have an edge in common are known as edge-disjoint Hamiltonian cycles (EDHCs). EDHCs are useful in computer networks. They have found applications in improving network capacity, fault-tolerance and collusion resistant mining algorithms. This paper extends previous work on collusion resistance capability of data mining algorithms. We first propose a new trust model for network computers. We then use this model as a basis to improve the collusion resistance capability of data mining algorithms. We use a performance metric to quantify the improvement.