A game theoretical approach for reputation propagation in online social networks

Formation of mutual trust between users of an Online Social Network (OSN ) is a function of many parameters. One of these parameters that has been widely investigated is the reputation of users. Users interact with each other with different intentions and as a result of their interactions they propagate each other's reputation. In the absence of centralized trusted parties in OSN s, the only way for an agent to estimate others' reputation is the other agents' thoughts about that agent. Therefore, intention and behavior of agents in the propagation of each other's reputation become crucial. In this thesis, we propose a game theoretic model of reputation propagation among users in OSN s. We use this model to first study the dynamics of propagation and then analyze users' behavior with respect to their reputation in the network. To do so, we expose the Nash equilibria of the proposed game. Finally, we develop some experiments on the large-scale social network of Epinons and compare our findings in the theoretical part with the observations from the experiments

ENHANCING PRIVACY IN MULTI-AGENT SYSTEMS

La pérdida de privacidad se está convirtiendo en uno de los mayores problemas en el mundo de la informática. De hecho, la mayoría de los usuarios de Internet (que hoy en día alcanzan la cantidad de 2 billones de usuarios en todo el mundo) están preocupados por su privacidad. Estas preocupaciones también se trasladan a las nuevas ramas de la informática que están emergiendo en los ultimos años. En concreto, en esta tesis nos centramos en la privacidad en los Sistemas Multiagente. En estos sistemas, varios agentes (que pueden ser inteligentes y/o autónomos) interactúan para resolver problemas. Estos agentes suelen encapsular información personal de los usuarios a los que representan (nombres, preferencias, tarjetas de crédito, roles, etc.). Además, estos agentes suelen intercambiar dicha información cuando interactúan entre ellos. Todo esto puede resultar en pérdida de privacidad para los usuarios, y por tanto, provocar que los usuarios se muestren adversos a utilizar estas tecnologías. En esta tesis nos centramos en evitar la colección y el procesado de información personal en Sistemas Multiagente. Para evitar la colección de información, proponemos un modelo para que un agente sea capaz de decidir qué atributos (de la información personal que tiene sobre el usuario al que representa) revelar a otros agentes. Además, proporcionamos una infraestructura de agentes segura, para que una vez que un agente decide revelar un atributo a otro, sólo este último sea capaz de tener acceso a ese atributo, evitando que terceras partes puedan acceder a dicho atributo. Para evitar el procesado de información personal proponemos un modelo de gestión de las identidades de los agentes. Este modelo permite a los agentes la utilización de diferentes identidades para reducir el riesgo del procesado de información. Además, también describimos en esta tesis la implementación de dicho modelo en una plataforma de agentes.Such Aparicio, JM. (2011). ENHANCING PRIVACY IN MULTI-AGENT SYSTEMS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/13023Palanci

Scalability of findability: decentralized search and retrieval in large information networks

Amid the rapid growth of information today is the increasing challenge for people to survive and navigate its magnitude. Dynamics and heterogeneity of large information spaces such as the Web challenge information retrieval in these environments. Collection of information in advance and centralization of IR operations are hardly possible because systems are dynamic and information is distributed. While monolithic search systems continue to struggle with scalability problems of today, the future of search likely requires a decentralized architecture where many information systems can participate. As individual systems interconnect to form a global structure, finding relevant information in distributed environments transforms into a problem concerning not only information retrieval but also complex networks. Understanding network connectivity will provide guidance on how decentralized search and retrieval methods can function in these information spaces. The dissertation studies one aspect of scalability challenges facing classic information retrieval models and presents a decentralized, organic view of information systems pertaining to search in large scale networks. It focuses on the impact of network structure on search performance and investigates a phenomenon we refer to as the Clustering Paradox, in which the topology of interconnected systems imposes a scalability limit. Experiments involving large scale benchmark collections provide evidence on the Clustering Paradox in the IR context. In an increasingly large, distributed environment, decentralized searches for relevant information can continue to function well only when systems interconnect in certain ways. Relying on partial indexes of distributed systems, some level of network clustering enables very efficient and effective discovery of relevant information in large scale networks. Increasing or reducing network clustering degrades search performances. Given this specific level of network clustering, search time is well explained by a poly-logarithmic relation to network size, indicating a high scalability potential for searching in a continuously growing information space

Information Sharing among Autonomous Agents in Referral Networks ⋆

Abstract. Referral networks are a kind of P2P system consisting of autonomous agents who seek and provide services, or refer other service providers. Key applications include service discovery and selection, and knowledge sharing. An agent seeking a service contacts other agents to discover suitable service providers. An agent who is contacted may autonomously ignore the request or respond by providing the desired service or giving a referral. This use of referrals is inspired by human interactions, where referrals are a key basis for judging the trustworthiness of a given service. The use of referrals differentiates such networks from traditional P2P information sharing systems, which are based on request flooding. Not only does the use of referrals enable an agent to control how its request is processed, it also provides an architectural basis for four kinds of interaction policies. InterPol is a language and framework supporting such policies. InterPol provides an ability to specify requests with hard and soft constraints as well as a vocabulary of application-independent terms based on interaction concepts. Using these, InterPol enables agents to reveal private information and accept others ’ information based on subtle relationships. In this manner, InterPol goes beyond traditional referral and other P2P systems in supporting practical applications. InterPol has been implemented using a Datalog-based policy engine for each agent. It has been applied on scenarios from a (multinational) health care project. The contribution of this paper is in a general referrals-based architecture for information sharing among autonomous agents, which is shown to effectively capture a variety of privacy and trust requirements of autonomous users.