PSH: A private and shared history-based incentive mechanism

Fully decentralized peer-to-peer (P2P) systems do not have a central control mechanism. Thus, different forms of control mechanisms are required to deal with selfish peers. One type of selfish behavior is the consumption of resources without providing sufficient resources. Therefore, incentive schemes encourage peers to share resources while punishing selfish peers. A well-known example of an incentive scheme is Tit-for-Tat (TFT), as used in BitTorrent. With this scheme, a peer can only consume as much resources as it provides. TFT is resilient to collusion due to relying on private histories only. However, TFT can only be applied to peers with direct reciprocity. This paper presents a private and shared history (PSH) based incentive mechanism, which supports transitive relations (indirect reciprocity). Furthermore, it is resilient to collusion and it combines private and shared histories in an efficient manner. The PSH approach uses a shared history for identifying transitive relations. Those relations are verified using private histories. Simulations show that the PSH mechanism has a higher transaction success ratio than TFT

Trust Management Survey

Trust is an important tool in human life, as it enables people to cope with the uncertainty caused by the free will of others. Uncertainty and uncontrollability are also issues in computer-assisted collaboration and electronic commerce in particular. A computational model of trust and its implementation can alleviate this problem. This surve

The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors

We developed an information-theoretic metric called the Interaction Index for prioritizing genetic variations and environmental variables for follow-up in detailed sequencing studies. The Interaction Index was found to be effective for prioritizing the genetic and environmental variables involved in GEI for a diverse range of simulated data sets. The metric was also evaluated for a 103-SNP Crohn's disease dataset and a simulated data set containing 9187 SNPs and multiple covariates that was modeled on a rheumatoid arthritis data set. Our results demonstrate that the Interaction Index algorithm is effective and efficient for prioritizing interacting variables for a diverse range of epidemiologic data sets containing complex combinations of direct effects, multiple GGI and GEI