METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

A Multi-dimensional Index Structure Based on Improved VA-file and CAN in the Cloud

Currently,the cloud computing systems use simple key-value data processing,which cannot support similarity search efectively due to lack of efcient index structures,and with the increase of dimensionality,the existing tree-like index structures could lead to the problem of"the curse of dimensionality".In this paper,a novel VF-CAN indexing scheme is proposed.VF-CAN integrates content addressable network(CAN)based routing protocol and the improved vector approximation fle(VA-fle) index.There are two index levels in this scheme:global index and local index.The local index VAK-fle is built for the data in each storage node.VAK-fle is thek-means clustering result of VA-fle approximation vectors according to their degree of proximity.Each cluster forms a separate local index fle and each fle stores the approximate vectors that are contained in the cluster.The vector of each cluster center is stored in the cluster center information fle of corresponding storage node.In the global index,storage nodes are organized into an overlay network CAN,and in order to reduce the cost of calculation,only clustering information of local index is issued to the entire overlay network through the CAN interface.The experimental results show that VF-CAN reduces the index storage space and improves query performance efectively.