Transparency and microaggregation

Transparency has an important effect on disclosure risk. In general, masking methods have to be evaluated taking into account that intruders can use all available information to attack the data. When the masking method as well as their parameters are disclosed, this information can also be used by an intruder. In this talk we will review results on the effects of transparency in disclosure risk assessment for microdata giving special emphasis to microaggregation

Eina per a la generació automàtica de consens : Master Thesis

Automatic knowledge acquisition for expert systems has attracted a lot of attention lately. Several algorithms for inferring a concept description from a given set of training examples have been developed to aid to this task. These algorithms infer with different training examples slighty different concept descriptions, therefore algorithms with different experts' data matrices would produce dissimiliar concept descriptions. We propose an approach based in J.Aczél and C. Alsina's Synthesis of judgements, Fuzzy logic and Classification methods.En els darrers anys ha estat molt estudiat el problema de l'adquisició de coneixement per a sistemes experts. Per ajudar a realitzar aquesta tasca s'han desenvolupat molts algorismes que infereixen descripcions de conceptes a partir d'un conjunt d'exemples, l'anomenat conjunt d'entrenament. Aquests algorismes infereixen en rebre exemples d'entrenament diferents, descripcions de conceptes també diferents, per tant algorismes amb matrius de dades de diferents experts produiran descripcions de conceptes no iguals. Aquí proposem una aproximació al consens basada en la teoria de J. Aczél i C. Alsina de la síntesis de judicis, la lògica difusa i els mètodes de classificació.Preprin

On the Protection of Social Network-Extracted Categorical Microdata

Social networks have become an essential part of the people’s com- munication system. They allow the users to express and share all the things they like with all the people they are connected with. However, this shared information can be dangerous for their privacy issues. In addition, there is some information that is not explicitly given but is implicit in the text of the posts that the user shares. For that reason, the information of each user needs to be protected. In this paper we present how implicit information can be extracted from the shared posts and how can we build a microdata dataset from a social network graph. Furthermore, we protect this dataset in order to make the users data more private.Peer reviewe

Modeling decisions: information fusion and aggregation operators

Information fusion techniques and aggregation operators produce the most comprehensive, specific datum about an entity using data supplied from different sources, thus enabling us to reduce noise, increase accuracy, summarize and extract information, and make decisions. These techniques are applied in fields such as economics, biology and education, while in computer science they are particularly used in fields such as knowledge-based systems, robotics, and data mining. This book covers the underlying science and application issues related to aggregation operators, focusing on tools used in practical applications that involve numerical information. Starting with detailed introductions to information fusion and integration, measurement and probability theory, fuzzy sets, and functional equations, the authors then cover the following topics in detail: synthesis of judgements, fuzzy measures, weighted means and fuzzy integrals, indices and evaluation methods, model selection, and parameter extraction. The methods are illustrated with representative examples throughout, and there are extensive bibliographies and reading suggestions. The book is intended for graduate students, researchers, and practitioners such as engineers, computer scientists, statisticians and economists who use decision models and aggregation operators. The reader is assumed to have a nonspecialized background in mathematics

Non-additive measures theory and applications

This book provides a comprehensive and timely report in the area of non-additive measures and integrals. It is based on a panel session on fuzzy measures, fuzzy integrals and aggregation operators held during the 9th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2012) in Girona, Spain, November 21-23, 2012. The book complements the MDAI 2012 proceedings book, published in Lecture Notes in Computer Science (LNCS) in 2012. The individual chapters, written by key researchers in the field, cover fundamental concepts and important definitions (e.g. the Sugeno integral, definition of entropy for non-additive measures) as well some important applications (e.g. to economics and game theory) of non-additive measures and integrals. The book addresses students, researchers and practitioners working at the forefront of their field.