`Q-Feed' - An Effective Solution for the Free-riding Problem in Unstructured P2P Networks

This paper presents a solution for reducing the ill effects of free-riders in decentralised unstructured P2P networks. An autonomous replication scheme is proposed to improve the availability and enhance system performance. Q-learning is widely employed in different situations to improve the accuracy in decision making by each peer. Based on the performance of neighbours of a peer, every neighbour is awarded different levels of ranks. At the same time a low-performing node is allowed to improve its rank in different ways. Simulation results show that Q-learning based free riding control mechanism effectively limits the services received by free-riders and also encourages the low-performing neighbours to improve their position. The popular files are autonomously replicated to nodes possessing required parameters. Due to this improvement of quantity of popular files, free riders are given opportunity to lift their position for active participation in the network for sharing files. Q-feed effectively manages queries from free riders and reduces network traffic significantlyComment: 14 pages, 10 figure

Reputation-based Trust Management in Peer-to-Peer File Sharing Systems

Trust is required in file sharing peer-to-peer (P2P) systems to achieve better cooperation among peers and reduce malicious uploads. In reputation-based P2P systems, reputation is used to build trust among peers based on their past transactions and feedbacks from other peers. In these systems, reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. This thesis surveys different reputation management systems with a focus on reputation based P2P systems. We breakdown a typical reputation system into functional components. We discuss each component and present proposed solutions from the literature. Different reputation-based systems are described and analyzed. Each proposed scheme presents a particular perspective in addressing peers’ reputation. This thesis also presents a novel trust management framework and associated schemes for partially decentralized file sharing P2P systems. We address trust according to three identified dimensions: Authentic Behavior, Credibility Behavior and Contribution Behavior. Within our trust management framework, we proposed several algorithms for reputation management. In particular, we proposed algorithms to detect malicious peers that send inauthentic files, and liar peers that send wrong feedbacks. Reputable peers need to be motivated to upload authentic files by increasing the benefits received from the system. In addition, free riders need to contribute positively to the system. These peers are consuming resources without uploading to others. To provide the right incentives for peers, we develop a novel service differentiation scheme based on peers’ contribution rather than peers’ reputation. The proposed scheme protects the system against free-riders and malicious peers and reduces the service provided to them. In this thesis, we also propose a novel recommender framework for partially decentralized file sharing P2P systems. We take advantage from the partial search process used in these systems to explore the relationships between peers. The proposed recommender system does not require any additional effort from the users since implicit rating is used. The recommender system also does not suffer from the problems that affect traditional collaborative filtering schemes like the Cold start, the Data sparseness and the Popularity effect. Over all, our unified approach to trust management and recommendations allows for better system health and increased user satisfaction

Motivational visualization for resources-sharing online communities

As online applications such as online newsgroups, internet game-rooms, online chat-rooms, and peer-to-peer (P2P) resources-sharing systems become popular, online community visualization became a hot research topic. Different forms and metaphors of visualizations focused on various aspects of online communities have been proposed. In this thesis, I propose one prototype of online community visualization which is designed to motivate user contributions in various aspects and stimulate users to participate in the online community more actively. The uneven participation is a well known problem in human society; according to the 80-20 rule, 20% of the people make 80% of contributions, for example, 20% of the employees in a company do 80% of the work. This problem exits in all kinds of online communities, e.g. newsgroups, chat-rooms, but it is particularly crucial for P2P online resources-sharing communities. Such communities do not have a central server and rely solely on the peers not just to provide contributions, but also to ensure the infrastructure. Large P2P file-sharing communities like KaZaA and Limewire can provide the redundancy of peers and resources needed to support the infrastructure and availability of resources. However, when an online community is small, for example, the students in a class, a research group, a department, or a school, the problem of lack of users it is hard to reach a “critical mass” of user participation, leading to poor service and resource availability, which reduces users’ interest in participating in the system. To attract users and motivate them to make more contributions into an online resources-sharing community, I propose to use motivational visualization of the community and the contributions of its members. The motivational effect of the visualization is grounded on two theories in social psychology which explain how individuals align their behaviour with each other and with their group (community). In this thesis, I discuss three stages in the design of the visualization and the subsequent redesigns following results from evaluation and user feedback

Contributions to security and privacy protection in recommendation systems

A recommender system is an automatic system that, given a customer model and a set of available documents, is able to select and offer those documents that are more interesting to the customer. From the point of view of security, there are two main issues that recommender systems must face: protection of the users' privacy and protection of other participants of the recommendation process. Recommenders issue personalized recommendations taking into account not only the profile of the documents, but also the private information that customers send to the recommender. Hence, the users' profiles include personal and highly sensitive information, such as their likes and dislikes. In order to have a really useful recommender system and improve its efficiency, we believe that users shouldn't be afraid of stating their preferences. The second challenge from the point of view of security involves the protection against a new kind of attack. Copyright holders have shifted their targets to attack the document providers and any other participant that aids in the process of distributing documents, even unknowingly. In addition, new legislation trends such as ACTA or the ¿Sinde-Wert law¿ in Spain show the interest of states all over the world to control and prosecute these intermediate nodes. we proposed the next contributions: 1.A social model that captures user's interests into the users' profiles, and a metric function that calculates the similarity between users, queries and documents. This model represents profiles as vectors of a social space. Document profiles are created by means of the inspection of the contents of the document. Then, user profiles are calculated as an aggregation of the profiles of the documents that the user owns. Finally, queries are a constrained view of a user profile. This way, all profiles are contained in the same social space, and the similarity metric can be used on any pair of them. 2.Two mechanisms to protect the personal information that the user profiles contain. The first mechanism takes advantage of the Johnson-Lindestrauss and Undecomposability of random matrices theorems to project profiles into social spaces of less dimensions. Even if the information about the user is reduced in the projected social space, under certain circumstances the distances between the original profiles are maintained. The second approach uses a zero-knowledge protocol to answer the question of whether or not two profiles are affine without leaking any information in case of that they are not. 3.A distributed system on a cloud that protects merchants, customers and indexers against legal attacks, by means of providing plausible deniability and oblivious routing to all the participants of the system. We use the term DocCloud to refer to this system. DocCloud organizes databases in a tree-shape structure over a cloud system and provide a Private Information Retrieval protocol to avoid that any participant or observer of the process can identify the recommender. This way, customers, intermediate nodes and even databases are not aware of the specific database that answered the query. 4.A social, P2P network where users link together according to their similarity, and provide recommendations to other users in their neighborhood. We defined an epidemic protocol were links are established based on the neighbors similarity, clustering and randomness. Additionally, we proposed some mechanisms such as the use SoftDHT to aid in the identification of affine users, and speed up the process of creation of clusters of similar users. 5.A document distribution system that provides the recommended documents at the end of the process. In our view of a recommender system, the recommendation is a complete process that ends when the customer receives the recommended document. We proposed SCFS, a distributed and secure filesystem where merchants, documents and users are protectedEste documento explora c omo localizar documentos interesantes para el usuario en grandes redes distribuidas mediante el uso de sistemas de recomendaci on. Se de fine un sistema de recomendaci on como un sistema autom atico que, dado un modelo de cliente y un conjunto de documentos disponibles, es capaz de seleccionar y ofrecer los documentos que son m as interesantes para el cliente. Las caracter sticas deseables de un sistema de recomendaci on son: (i) ser r apido, (ii) distribuido y (iii) seguro. Un sistema de recomendaci on r apido mejora la experiencia de compra del cliente, ya que una recomendaci on no es util si es que llega demasiado tarde. Un sistema de recomendaci on distribuido evita la creaci on de bases de datos centralizadas con informaci on sensible y mejora la disponibilidad de los documentos. Por ultimo, un sistema de recomendaci on seguro protege a todos los participantes del sistema: usuarios, proveedores de contenido, recomendadores y nodos intermedios. Desde el punto de vista de la seguridad, existen dos problemas principales a los que se deben enfrentar los sistemas de recomendaci on: (i) la protecci on de la intimidad de los usuarios y (ii) la protecci on de los dem as participantes del proceso de recomendaci on. Los recomendadores son capaces de emitir recomendaciones personalizadas teniendo en cuenta no s olo el per l de los documentos, sino tambi en a la informaci on privada que los clientes env an al recomendador. Por tanto, los per les de usuario incluyen informaci on personal y altamente sensible, como sus gustos y fobias. Con el n de desarrollar un sistema de recomendaci on util y mejorar su e cacia, creemos que los usuarios no deben tener miedo a la hora de expresar sus preferencias. Para ello, la informaci on personal que est a incluida en los per les de usuario debe ser protegida y la privacidad del usuario garantizada. El segundo desafi o desde el punto de vista de la seguridad implica un nuevo tipo de ataque. Dado que la prevenci on de la distribuci on ilegal de documentos con derechos de autor por medio de soluciones t ecnicas no ha sido efi caz, los titulares de derechos de autor cambiaron sus objetivos para atacar a los proveedores de documentos y cualquier otro participante que ayude en el proceso de distribuci on de documentos. Adem as, tratados y leyes como ACTA, la ley SOPA de EEUU o la ley "Sinde-Wert" en España ponen de manfi esto el inter es de los estados de todo el mundo para controlar y procesar a estos nodos intermedios. Los juicios recientes como MegaUpload, PirateBay o el caso contra el Sr. Pablo Soto en España muestran que estas amenazas son una realidad

Location-aware mechanism for efficient video delivery over wireless mesh networks

Due to their flexibility, ease of use, low-cost and fast deployment, wireless Mesh Networks have been widely accepted as an alternative to wired network for last-mile connectivity. When used in conjunction with Peer-to-Peer data transfer solutions, many innovative applications and services such as distributed storage, resource sharing, live TV broadcasting or Video on Demand can be supported without any centralized administration. However, in order to achieve a good quality of service in such variable, error-prone and resource-constrained wireless multi-hop environments, it is important that the associated Peer-to-Peer overlay is not only aware of the availability, but also of the location and available path link quality of its peers and services. This thesis proposes a wireless location-aware Chord-based overlay mechanism for Wireless Mesh Networks (WILCO) based on a novel geographical multi-level ID mapping and an improved finger table. The proposed scheme exploits the location information of mesh routers to decrease the number of hops the overlay messages traverse in the physical topology. Analytical and simulation results demonstrate that in comparison to the original Chord, WILCO has significant benefits: it reduces the number of lookup messages, has symmetric lookup on keys in both the forward and backward direction of the Chord ring and achieves a stretch factor of O(1). On top of this location-aware overlay, a WILCO-based novel video segment seeking algorithm is proposed to make use of the multi-level WILCO ID location-awareness to locate and retrieve requested video segments from the nearest peer in order to improve video quality. An enhanced version of WILCO segment seeking algorithm (WILCO+) is proposed to mitigate the sometimes suboptimal selection of the WILCO video segment seeking algorithm by extracting coordinates from WILCO ID to enable location-awareness. Analytical and simulation results illustrate that the proposed scheme outperforms the existing state-of-the-art solutions in terms of PSNR and packet loss with different background traffic loads. While hop count is frequently strongly correlated to Quality of Service, the link quality of the underlying network will also have a strong influence on content retrieval quality. As a result, a Cross-layer Wireless Link Quality-aware Overlay peer selection mechanism (WLO) is proposed. The proposed cross-layer mechanism uses a Multiplication Selector Metric (MSM) to select the best overlay peer. The proposed MSM overcomes the two issues facing the traditional summation-based metric, namely, the difficulty of bottleneck link identification and the influence of hop count on behavior. Simulation results show that WLO outperforms the existing state-of-the-art solutions in terms of video quality at different background loads and levels of topology incompleteness. Real life emulation-based tests and subjective video quality assessments are also performed to show that the simulation results are closely matched by the real-life emulation-based results and to illustrate the significant impact of overlay peer selection on the user perceived video quality

Combining MAS and P2P systems : the Agent Trees Multi-Agent System (ATMAS)

The seamless retrieval of information distributed across networks has been one of the key goals of many systems. Early solutions involved the use of single static agents which would retrieve the unfiltered data and then process it. However, this was deemed costly and inefficient in terms of the bandwidth since complete files need to be downloaded when only a single value is often all that is required. As a result, mobile agents were developed to filter the data in situ before returning it to the user. However, mobile agents have their own associated problems, namely security and control. The Agent Trees Multi-Agent System (AT-MAS) has been developed to provide the remote processing and filtering capabilities but without the need for mobile code. It is implemented as a Peer to Peer (P2P) network of static intelligent cooperating agents, each of which control one or more data sources. This dissertation describes the two key technologies have directly influenced the design of ATMAS, Peer-to-Peer (P2P) systems and Multi-Agent Systems (MAS). P2P systems are conceptually simple, but limited in power, whereas MAS are significantly more complex but correspondingly more powerful. The resulting system exhibits the power of traditional MAS systems while retaining the simplicity of P2P systems. The dissertation describes the system in detail and analyses its performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo