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.

A well-known problem in P2P systems is freeriding, where users do not share content if there is no incentive to do so. In this paper, we distinguish lazy freeriders that are merely reluctant to share but follow the protocol, versus die-hard freeriders that employ sophisticated methods to subvert the protocol. Existing incentive designs often provide theoretically attractive resistance against die-hard freeriding, yet are rarely deployed in real networks because of practical infeasibility. Meanwhile, real communities benefit greatly from prevention of lazy freeriding, but have only centralized technology available to do so. We present a lightweight, fully distributed mechanism called BARTERCAST that prevents lazy freeriding and is deployed in practice. BarterCast uses a maxflow reputation algorithm based on a peer’s private history of its data exchanges as well as indirect information received from other peers. We assess different reputation policies under realistic, trace-based community conditions and show that our mechanism is consistent and effective, even when significant fractions of peers spread false information. Furthermore, we present results of the deployment of BarterCast in the BitTorrent-based Tribler network which currently has thousands of users worldwide. 1.

We consider problems of provisioning an excludable public good amongst n potential members of a peer-to-peer system who are able to communicate information about their private preferences for the good. The cost of provisioning the good in quantity Q depends on Q, and may also depend on n, or on the final number of participating peers, m. Our aim is to maximize social welfare in a way that is incentive compatible, rational and feasible. Although it is unfortunately almost never possible to calculate or implement a truely optimal Mechanism Design, we show that as the number of participants becomes large the expected social welfare that can be obtained by the optimal design is at most a factor 1 + O(1/n) or 1 + O(1 / √ n) greater than that which can be obtained with a very simple scheme that requires only a fixed contribution (payment) from any agent who joins the system as a participating peer. Our first application is to a model of file sharing, in which the public good is content availability; the second concerns a problem of peering wireless LANs, in which the public good is the availability of connectivity for roaming peers. In both problems we can cope with the requirement that the payments be made in kind, rather than in cash.