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

Implementing MAS agreement processes based on consensus networks

[EN] Consensus is a negotiation process where agents need to agree upon certain quantities of interest. The theoretical framework for solving consensus problems in dynamic networks of agents was formally introduced by Olfati-Saber and Murray, and is based on algebraic graph theory, matrix theory and control theory. Consensus problems are usually simulated using mathematical frameworks. However, implementation using multi-agent system platforms is a very difficult task due to problems such as synchronization, distributed finalization, and monitorization among others. The aim of this paper is to propose a protocol for the consensus agreement process in MAS in order to check the correctness of the algorithm and validate the protocol. © Springer International Publishing Switzerland 2013.This work is supported by ww and PROMETEO/2008/051 projects of the Spanish government, CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, TIN2012-36586-C03-01 and PAID-06-11-2084.Palomares Chust, A.; Carrascosa Casamayor, C.; Rebollo Pedruelo, M.; Gómez, Y. (2013). Implementing MAS agreement processes based on consensus networks. Distributed Computing and Artificial Intelligence. 217:553-560. https://doi.org/10.1007/978-3-319-00551-5_66S553560217Argente, E.: et al: An Abstract Architecture for Virtual Organizations: The THOMAS approach. Knowledge and Information Systems 29(2), 379–403 (2011)Búrdalo, L.: et al: TRAMMAS: A tracing model for multiagent systems. Eng. Appl. Artif. Intel. 24(7), 1110–1119 (2011)Fogués, R.L., et al.: Towards Dynamic Agent Interaction Support in Open Multiagent Systems. In: Proc. of the 13th CCIA, vol. 220, pp. 89–98. IOS Press (2010)Luck, M., et al.: Agent technology: Computing as interaction (a roadmap for agent based computing). Eng. Appl. Artif. Intel. (2005)Mailler, R., Lesser, V.: Solving distributed constraint optimization problems using cooperative mediation. In: AAMAS 2004, pp. 438–445 (2004)Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE 95(1), 215–233 (2007)Pujol-Gonzalez, M.: Multi-agent coordination: Dcops and beyond. In: Proc. of IJCAI, pp. 2838–2839 (2011)Such, J.: et al: Magentix2: A privacy-enhancing agent platform. Eng. Appl. Artif. Intel. 26(1), 96–109 (2013)Vinyals, M., et al.: Constructing a unifying theory of dynamic programming dcop algorithms via the generalized distributive law. Autonomous Agents and Multi-Agent Systems 22, 439–464 (2011

Privacy Management Service Contracts as a New Business Opportunity for Operators

Recognizing the importance of privacy management as a business process and a business support process, this paper proposes the use of service level agreements (SLA’s) around privacy features, including qualitative and quantitative ones. Privacy metrics are defined by both parties with boundary values on each qualitative or qualitative feature. Their distribution is relying on stress distributions used in this field. The use of service level agreements also casts privacy management into a business perspective with benefits and costs to either party in a process. This approach is especially relevant for communications operators as brokers between content owners (individuals, businesses) and enterprise applications; in this context, the privacy SLA management would be carried out by the operator, while the terms and conditions of the SLA negotiation reside with the two external parties. This work was carried out as part of the large EU project PRIME www.prime.project.eu.org. on privacy enhancing technologies.Content Owners;Enterprise Business Processes;Managed Service Contracts;Privacy Agreements;Service Level Agreements (SLA's);Telecommunications Operators

Privacy, security, and trust issues in smart environments

Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning