In a basic economic system, each participant receives a (financial) reward according to his own contribution to the system. In this work, we study an alternative approach — Incentive Networks — in which a participant's reward depends not only on his own contribution; but also in part on the contributions made by his social contacts or friends. We show that the key parameter effecting the efficiency of such an Incentive Network-based economic system depends on the participant's degree of directed altruism. Directed altruism is the extent to which someone is willing to work if his work results in a payment to his friend, rather than to himself. Specifically, we characterize the condition under which an Incentive Network-based economy is more efficient than the basic "pay-for-your-contribution" economy. We quantify by how much incentive networks can reduce the total reward that needs to be paid to the participants in order to achieve a certain overall contribution. Finally, we study the impact of the network topology and various exogenous parameters on the efficiency of incentive networks. Our results suggest that in many practical settings, Incentive Network-based reward systems or compensation structures could be more efficient than the ubiquitous 'pay-for-your-contribution' schemes.

We study Incentive Tree for motivating the participation of people in crowdsourcing or human tasking systems. In an Incentive Tree, each participant is rewarded for contributing to the system, as well as for soliciting new participants into the system, who then themselves contribute to it and/or themselves solicit new participants. An Incentive Tree mechanism is an algorithm that determines how much reward each individual participant receives based on all the participants' contributions, as well as the structure of the solicitation tree. The sum of rewards paid by the mechanism to all participants is linear in the sum of their total contribution.

Accuracy and speed are the two most important metrics for Network Intrusion Detection/Prevention Systems (NIDS/NIPSes). Due to emerging polymorphic attacks and the fact that in many cases regular expressions (regexes) cannot capture the vulnerability conditions accurately, the accuracy of existing regex-based NIDS/NIPS systems has become a serious problem. In contrast, the recently-proposed vulnerability signatures (a.k.a data patches) can exactly describe the vulnerability conditions and achieve better accuracy. However, how to efficiently apply vulnerability signatures to high speed NIDS/NIPS with a large ruleset remains an untouched but challenging issue.