Establishing trust measurements among peer-to-peer (P2P) networks is fast becoming a de-facto standard, and a fair amount of work has been done in the area of trust aggregation and calculation algorithms. However, the area of developing secure underlying protocols to distribute and access the trust ratings in the overlay network has been relatively unexplored. We propose an elliptic curve-based trust management protocol for P2P systems, which is designed to provide authentication and signature functions to protect the processes of trust value query and rating report. Additionally, instead of using single identities, the protocol generates two verifiable pseudonyms, one is used for transaction, the other is applied when the peer acts as a trust holding peer. A security analysis shows that the proposed protocol is extremely secure in the face of a variety of possible attacks.

A new training algorithm for the chaotic Adachi Neural Network (AdNN) is investigated. The classical training algorithm for the AdNN and it's variants is usually a “one-shot” learning, for example, the Outer Product Rule (OPR) is the most used. Although the OPR is effective for conventional neural networks, its effectiveness and adequateness for Chaotic Neural Networks (CNNs) have not been discussed formally. As a complementary and tentative work in this field, we modified the AdNN's weights by enforcing an unsupervised Hebbian rule. Experimental analysis shows that the new weighted AdNN yields even stronger dynamical associative memory and pattern recognition phenomena for different settings than the primitive AdNN.