Fully Distributed Cooperative Spectrum Sensing for Cognitive Radio Networks

Cognitive radio networks (CRN) sense spectrum occupancy and manage themselves to operate in unused bands without disturbing licensed users. The detection capability of a radio system can be enhanced if the sensing process is performed jointly by a group of nodes so that the effects of wireless fading and shadowing can be minimized. However, taking a collaborative approach poses new security threats to the system as nodes can report false sensing data to force a wrong decision. Providing security to the sensing process is also complex, as it usually involves introducing limitations to the CRN applications. The most common limitation is the need for a static trusted node that is able to authenticate and merge the reports of all CRN nodes. This paper overcomes this limitation by presenting a protocol that is suitable for fully distributed scenarios, where there is no static trusted node

Supervised learning using a symmetric bilinear form for record linkage

Record Linkage is used to link records of two different files corresponding to the same individuals. These algorithms are used for database integration. In data privacy, these algorithms are used to evaluate the disclosure risk of a protected data set by linking records that belong to the same individual. The degree of success when linking the original (unprotected data) with the protected data gives an estimation of the disclosure risk. In this paper we propose a new parameterized aggregation operator and a supervised learning method for disclosure risk assessment. The parameterized operator is a symmetric bilinear form and the supervised learning method is formalized as an optimization problem. The target of the optimization problem is to find the values of the aggregation parameters that maximize the number of re-identification (or correct links). We evaluate and compare our proposal with other non-parametrized variations of record linkage, such as those using the Mahalanobis distance and the Euclidean distance (one of the most used approaches for this purpose). Additionally, we also compare it with other previously presented parameterized aggregation operators for record linkage such as the weighted mean and the Choquet integral. From these comparisons we show how the proposed aggregation operator is able to overcome or at least achieve similar results than the other parameterized operators. We also study which are the necessary optimization problem conditions to consider the described aggregation functions as metric functions

Aprendizaje supervisado para el enlace de registros a través de la media ponderada

En el área de la privacidad de datos, las técnicas para el enlace de registros son utilizadas para evaluar el riesgo de revelación de un conjunto de datos protegido. La idea principal detrás de estas técnicas es enlazar registros que hacen referencia a un mismo individuo, entre diferentes bases de datos. En este trabajo se presenta una variación del enlace de registros basada en una media ponderada para calcular distancias entre registros. Mediante el uso de un método supervisado de aprendizaje nuestra propuesta permite determinar cuáles son los pesos que maximizan el número de enlaces entre los registros de la base de datos original y su versión protegida. El resultado de este trabajo se aplica en la estimación del riesgo de revelación de datos protegidos.Esta investigación está parcialmente financiada por el MICINN (proyectos ARES-CONSOLIDER INGENIO 2010 CSD2007-00004, TIN2010-15764 y TIN2011-27076-C03-03) y por by the EC (FP7/2007-2013) Data without Boundaries (número de subvención 262608). Algunos de los resultados presentados en este artículo han sido obtenidos gracias al Centro de Supercomputación de Galicia (CESGA). El trabajo contribuido por el primer autor ha sido parte de un programa de doctorado en Informática de la Universidad Autónoma de Barcelona (UAB)

Aprendizaje supervisado para el enlace de registros a través de la media ponderada

En el área de la privacidad de datos, las técnicas para el enlace de registros son utilizadas para evaluar el riesgo de revelación de un conjunto de datos protegido. La idea principal detrás de estas técnicas es enlazar registros que hacen referencia a un mismo individuo, entre diferentes bases de datos. En este trabajo se presenta una variación del enlace de registros basada en una media ponderada para calcular distancias entre registros. Mediante el uso de un método supervisado de aprendizaje nuestra propuesta permite determinar cuáles son los pesos que maximizan el número de enlaces entre los registros de la base de datos original y su versión protegida. El resultado de este trabajo se aplica en la estimación del riesgo de revelación de datos protegidos.Esta investigacion esta parcialmente financiada por el MICINN (proyectos ARES-CONSOLIDER INGENIO 2010 CSD2007-00004, TIN2010-15764 y TIN2011-27076-C03-03) y por by the EC (FP7/2007-2013) Data without Boundaries (numero de subvencion 262608). Algunos de los resultados presentados en este artıculo han sido obtenidos gracias al Centro de Supercomputacion de Galicia (CESGA). El trabajo contribuido por el primer autor ha sido parte de un programa de doctorado en Informatica de la Universidad Autonoma de Barcelona (UAB).Peer Reviewe

Fuzzy role-based access control

RBAC (Role-Based Access Control) is a widely used access control model, which reduces the maintenance cost of classical identity-based access control. However, despite the benefits of RBAC, there are environments in which RBAC can hardly be applied. We present FRBAC (Fuzzy Role-Based Access Control), a generalization of RBAC through fuzzy relations that extends the applicability of RBAC to environments where authorization-related information is vague. Moreover, FRBAC deals with environments where the actions that can be executed over the resources have a fractional meaning, as data lying in databases and risk-based access control. © 2011 Elsevier B.V. All rights reserved.Partial support by the Spanish MICINN (projects TSI2007-65406-C03-02, ARES- CONSOLIDER INGENIO 2010 CSD2007-00004) and Universitat Autonoma de Barcelona (PIF472-01-1/07) is acknowledged.Peer Reviewe

Fuzzy role-based access control

RBAC (Role-Based Access Control) is a widely used access control model, which reduces the maintenance cost of classical identity-based access control. However, despite the benefits of RBAC, there are environments in which RBAC can hardly be applied. We present FRBAC (Fuzzy Role-Based Access Control), a generalization of RBAC through fuzzy relations that extends the applicability of RBAC to environments where authorization-related information is vague. Moreover, FRBAC deals with environments where the actions that can be executed over the resources have a fractional meaning, as data lying in databases and risk-based access control

Semantic microaggregation for the anonymization of query logs using the open directory project

Web search engines gather information from the queries performed by the user in the form of query logs. These logs are extremely useful for research, marketing, or profiling, but at the same time they are a great threat to the user’s privacy. We provide a novel approach to anonymize query logs so they ensure user k-anonymity, by extending a common method used in statistical disclosure control: microaggregation. Furthermore, our microaggregation approach takes into account the semantics of the queries by relying on the Open Directory Project. We have tested our proposal with real data from AOL query logsPeer Reviewe

A bloom filter based model for decentralized authorization

A decentralized authorization mechanism is proposed that uses Bloom filters to implement authorization delegation. This lightweight mechanism is unlike conventional approaches that typically rely on public key certificates to implement distributed delegation. In taking an approach based on one-way hash functions, the mechanism may be preferable for use in computationally constrained environments where public-key cryptography is not desirable

Data privacy

Data privacy studies methods, tools, and theory to avoid the disclosure of sensitive information. Its origin is in statistics with the goal to ensure the confidentiality of data gathered from census and questionnaires. The topic was latter introduced in computer science and more particularly in data mining, where due to the large amount of data currently available, has attracted the interest of researchers, practitioners, and companies. In this paper we will review the main topics related to data privacy and privacy-enhancing technologies