In a decentralized storage system, agents replicate each other’s data to increase availability. Compared to organizationally centralized solutions, such as cloud storage, a decentralized storage system requires less trust in the provider and may result in smaller monetary costs. Our system is based on reciprocal storage contracts that allow the agents to adopt to changes in their replication partners’ availability (by dropping inefficient contracts and forming new contracts with other partners). The data availability provided by the system is a function of the participating agents’ availability. However, a straightforward system in which agents’ matching is decentralized uses the given agent availability inefficiently. As agents are autonomous, the highly available agents form cliques replicating data between each other, which makes the system too hostile for the weakly available newcomers. In contrast, a centralized, equitable matching is not incentive compatible: it does not reward users for keeping their software running. We solve this dilemma by a mixed solution: an “adoption” mechanism in which highly available agents donate some replication space, which in turn is used to help the worst-off agents. We show that the adoption motivates agents to increase their availability (is incentive-compatible), but also that it is sufficient for acceptable data availability for weakly-available agents.

One of the differences between typical peer-to-peer (P2P) and client-server systems is the existence of user accounts. While many P2P applications, like public file sharing, are anonymous, more complex services such as decentralized online social networks require user authentication. In these, the common approach to P2P authentication builds on the possession of cryptographic keys. A drawback with that approach is usability when users access the system from multiple devices, an increasingly common scenario. In this work, we present a scheme to support logins based on users knowing a username-password pair. We use passwords, as they are the most common authentication mechanism in services on the Internet today, ensuring strong user familiarity. In addition to password logins, we also present supporting protocols to provide functionality related to password logins, such as resetting a forgotten password via e-mail or security questions. Together, these allow P2P systems to emulate centralized password logins. The results of our performance evaluation indicate that incurred delays are well within acceptable bounds.

Vehicular networks (VANETs) can be used to improve transportation security, reliability, and management. This paper investigates security aspects of VANETs within a game-theoretic framework where defensive measures are optimized with respect to threats posed by malicious attackers. The formulations are chosen to be abstract on purpose in order to maximize applicability of the models and solutions to future systems. The security games proposed for vehicular networks take as an input centrality measures computed by mapping the centrality values of the car networks to the underlying road topology. The resulting strategies help locating most valuable or vulnerable points (e.g., against jamming) in vehicular networks. Thus, optimal deployment of traffic control and security infrastructure is investigated both in the static (e.g., fixed roadside units) and dynamic cases (e.g., mobile law enforcement units). Multiple types of security games are studied under varying information availability assumptions for the players, leading to fuzzy game and fictitious play formulations in addition to classical zero-sum games. The effectiveness of the security game solutions is evaluated numerically using realistic simulation data obtained from traffic engineering systems.

In peer-to-peer storage systems, peers replicate each others' data in order to increase availability. If the matching is done centrally, the algorithm can optimize data availability in an equitable manner for all participants. However, if matching is decentralized, the peers' selfishness can greatly alter the results, leading to performance inequities that can render the system unreliable and thus ultimately unusable. We analyze the problem using both theoretical approaches (complexity analysis for the centralized system, game theory for the decentralized one) and simulation. We prove that the problem of optimizing availability in a centralized system is NP-hard. In decentralized settings, we show that the rational behavior of selfish peers will be to replicate only with similarly-available peers. Compared to the socially-optimal solution, highly available peers have their data availability increased at the expense of decreased data availability for less available peers. The price of anarchy is high: unbounded in one model, and linear with the number of time slots in the second model. We also propose centralized and decentralized heuristics that, according to our experiments, converge fast in the average case. The high price of anarchy means that a completely decentralized system could be too hostile for peers with low availability, who could never achieve satisfying replication parameters. Moreover, we experimentally show that even explicit consideration and exploitation of diurnal patterns of peer availability has a small effect on the data availability-except when the system has truly global scope. Yet a fully centralized system is infeasible, not only because of problems in information gathering, but also the complexity of optimizing availability. The solution to this dilemma is to create system-wide cooperation rules that allow a decentralized algorithm, but also limit the selfishness of the participants.

We study a new application of threshold-based secret sharing in a distributed online social network (DOSN), where users need a means to back up and recover their private keys in a network of untrusted servers. Using a simple threshold-based secret sharing in such an environment is insufficiently secured since delegates keeping the secret shares may collude to steal the user's private keys. To mitigate this problem, we propose using different techniques to improve the system security: by selecting only the most reliable delegates for keeping these shares and further by encrypting the shares with passwords. We develop a mechanism to select the most reliable delegates based on an effective trust measure. Specifically, relationships among the secret owner, delegate candidates and their related friends are used to estimate the trustworthiness of a delegate. This trust measure minimizes the likelihood of the secret being stolen by an adversary and is shown to be effective against various collusive attacks. Extensive simulations show that the proposed trust-based delegate selection performs very well in highly vulnerable environments where the adversary controls many nodes with different distributions and even with spreading of infections in the network. In fact, the number of keys lost is very low under extremely pessimistic assumptions of the adversary model.

In this paper, we present a horizontal view of social influence, more specifically a quantitative study of the influence of neighbours on the probability of a particular node to join a group, on four popular Online Social Networks (OSNs), namely Orkut, YouTube, LiveJournal, and Flickr. Neighbours in OSNs have a mutually acknowledged relation, most often defined as friendship, and they are directly connected on a graph of a social network. Users in OSNs can also join groups of users. These groups represent common areas of interest.We present a simple social influence model to describe and explain the group joining process of users on OSNs. To this end, we extract the social influence from data sets of OSNs of a million sample nodes. One of our findings is that a set of neighbours in the OSN is about 100 times more powerful in influencing a user to join a group than the same number of strangers.

Mobile ad-hoc networking works properly only if the participating nodes cooperate in routing and forwarding. However,it may be advantageous for individual nodes not to cooperate. We propose a protocol, called CONFIDANT, for making misbehavior unattractive; it is based on selective altruism and utilitarianism. It aims at detecting and isolating misbehaving nodes, thus making it unattractive to deny cooperation. Trust relationships and routing decisions are based on experienced, observed, or reported routing and forwarding behavior of other nodes. The detailed implementation of CONFIDANT in this paper assumes that the network layer is based on the Dynamic Source Routing (DSR) protocol. We present a performance analysis of DSR fortified by CONFIDANT and compare it to regular defenseless DSR. It shows that a network with CONFIDANT and up to 60% of misbehaving nodes behaves almost as well as a benign network, in sharp contrast to a defenseless network. All simulations have been implemented and performed in GloMoSim.