Lack of cooperation (free riding) is one of the key problems that confronts today's P2P systems. What makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors. To tackle these challenges we model the P2P system using the Generalized Prisoner's Dilemma (GPD), and propose the Reciprocative decision function as the basis of a family of incentives techniques. These techniques are fully distributed and include: discriminating server selection, maxflowbased subjective reputation, and adaptive stranger policies. Through simulation, we show that these techniques can drive a system of strategic users to nearly optimal levels of cooperation.

-We devise a simple model to study the phenomenon of free-riding and the effect of free identities on user behavior in peer-to-peer systems. At the heart of our model is a strategic user of a certain type, an intrinsic and private parameter that reflects the user's generosity. The user decides whether to contribute or free-ride based on how the current burden of contributing in the system compares to her type. We derive the emerging cooperation level in equilibrium and quantify the effect of providing free-riders with degraded service on the emerging cooperation. We find that this penalty mechanism is beneficial mostly when the "generosity level" of the society (i.e., the average type) is low. To quantify the social cost of free identities, we extend the model to account for dynamic scenarios with turnover (users joining and leaving) and with whitewashers: users who strategically leave the system and re-join with a new identity. We find that the imposition of penalty on all legitimate newcomers incurs a significant social loss only under high turnover rates in conjunction with intermediate societal generosity levels.

Abstract. With the embedding of EEG (electro-encephalography) sensors in wireless headsets and other consumer electronics, authenticating users based on their brainwave signals has become a realistic possibility. We undertake an experimental study of the usability and performance of user authentication using consumer-grade EEG sensor technology. By choosing custom tasks and custom acceptance thresholds for each subject, we can achieve 99% authentication accuracy using single-channel EEG signals, which is on par with previous research employing multichannel EEG signals using clinical-grade devices. In addition to the usability improvement offered by the single-channel dry-contact EEG sensor, we also study the usability of different classes of mental tasks. We find that subjects have little difficulty recalling chosen "pass-thoughts" (e.g., their previously selected song to sing in their mind). They also have different preferences for tasks based on the perceived difficulty and enjoyability of the tasks. These results can inform the design of authentication systems that guide users in choosing tasks that are both usable and secure.

While the fundamental premise of peer-to-peer (P2P) systems is that of voluntary resource sharing among individual peers, there is an inherent tension between individual rationality and collective welfare that threatens the viability of these systems. This paper surveys recent research at the intersection of economics and computer science that targets the design of distributed systems consisting of rational participants with diverse and selfish interests. In particular, we discuss major findings and open questions related to free-riding in P2P systems: factors affecting the degree of free-riding, incentive mechanisms to encourage user cooperation, and challenges in the design of incentive mechanisms for P2P systems.

Copyright holders have been investigating technological solutions to prevent distribution of copyrighted materials in peer-to-peer file sharing networks. A particularly popular technique consists in "poisoning" a specific item (movie, song, or software title) by injecting a massive number of decoys into the peer-to-peer network, to reduce the availability of the targeted item. In addition to poisoning, pollution, that is, the accidental injection of unusable copies of files in the network, also decreases content availability. In this paper, we attempt to provide a first step toward understanding the differences between pollution and poisoning, and their respective impact on content availability in peer-to-peer file sharing networks. To that effect, we conduct a measurement study of content availability in the four most popular peer-to-peer file sharing networks, in the absence of poisoning, and then simulate different poisoning strategies on the measured data to evaluate their potential impact. We exhibit a strong correlation between content availability and topological properties of the underlying peer-to-peer network, and show that the injection of a small number of decoys can seriously impact the users' perception of content availability.

-We devise a simple model to study the phenomenon of free-riding and the effect of free identities on user behavior in peer-to-peer systems. At the heart of our model is a strategic user of a certain type, an intrinsic and private parameter that reflects the user's generosity. The user decides whether to contribute or free-ride based on how the current burden of contributing in the system compares to her type. We derive the emerging cooperation level in equilibrium and quantify the effect of providing free-riders with degraded service on the emerging cooperation. We find that this penalty mechanism is beneficial mostly when the "generosity level" of the society (i.e., the average type) is low. To quantify the social cost of free identities, we extend the model to account for dynamic scenarios with turnover (users joining and leaving) and with whitewashers: users who strategically leave the system and re-join with a new identity. We find that the imposition of penalty on all legitimate newcomers incurs a significant social loss only under high turnover rates in conjunction with intermediate societal generosity levels.

Abstract. Wireless sensor networks will be used in a wide range of challenging applications where numerous sensor nodes are linked to monitor and report distributed event occurrences. In contrast to traditional communication networks, the single major resource constraint in sensor networks is power, due to the limited battery life of sensor devices. It has been shown that data-centric methodologies can be used to solve this problem efficiently. In data-centric storage, a recently proposed data dissemination framework, all event data is stored by type at designated nodes in the network and can later be retrieved by distributed mobile access points in the network. In this paper we propose Resilient Data-Centric Storage (R-DCS) as a method to achieve scalability and resilience by replicating data at strategic locations in the sensor network. Through analytical results and simulations, we show that this scheme leads to significant energy savings in reasonably large-sized networks and scales well with increasing node-density and query rate. We also show that R-DCS realizes graceful performance degradation in the presence of clustered as well as isolated node failures, hence making the sensornet data robust.

Abstract-We propose a service differentiated peer selection mechanism for peer-to-peer media streaming systems. The mechanism provides flexibility and choice in peer selection to the contributors of the system, resulting in high quality streaming sessions. Free-riders are given limited options in peer selection, if any, and hence receive low quality streaming. The proposed incentive mechanism follows the characteristics of rank-order tournaments theory that considers only the relative performance of the players, and the top prizes are awarded to the winners of the tournament. Using rank-order tournaments, we analyze the behavior of utility maximizing users. Through simulation and wide-area measurement studies, we verify that the proposed incentive mechanism can provide near optimal streaming quality to the cooperative users until the bottleneck shifts from the streaming sources to the network.