An Evolutionary Optimization Approach for Categorical Data Protection

The continuous growing amount of public sensible data has increased the risk of breaking the privacy of people or institutions in those datasets. Many protection methods have been developed to solve this problem by either distorting or generalizing data but taking into account the difficult tradeoff between data utility (information loss) and protection against disclosure (disclosure risk). In this paper we present an optimization approach for data protection based on an evolutionary algorithm which is guided by a combination of information loss and disclosure risk measures. In this way, state-of-the-art protection methods are combined to obtain new data protections with a better trade-off between these two measures. The paper presents several experimental results that assess the performance of our approach

An evolutionary algorithm to enhance multivariate Post-Randomization Method (PRAM) protections

The amount of public statistical information available is growing and more accurate protection methods are needed in order to achieve data confidentiality. The Post-Randomization Method (PRAM) protection method was introduced in 1997 as a very powerful method for categorical microdata, but it is still not widely used. This method has a Markov matrix as a parameter. The main problem of the application of this method is that it is difficult to find a good Markov matrix that performs changes in the microdata file producing low loss of valuable information and low risk of disclosure of sensitive data. In this paper we present a methodology that helps us to find a matrix to perform better protections. This is achieved by using an evolutionary algorithm with integrated Information Loss and Disclosure Risk measures. Experiments using three different datasets are also presented in order to empirically evaluate the application of this technique. © 2014 Elsevier Inc. All rights reserved.This work has been done under the PhD in Computer Science program of the Universitat Autònoma de Barcelona (UAB). It is also partially supported by the Spanish MEC ARES-CONSOLIDER INGENIO 2010 CSD2007-00004, and COPRIVACY TIN2011-27076-C03-03. The research leading to these results has also received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Num. 262608.Peer Reviewe

Automatic instrument localization in laparoscopic surgery

This paper presents a tracking algorithm for automatic instrument localization in robotically assisted laparoscopic surgery. We present a simple and robust system that doesn't need the presence of artificial marks, or special colours to distinguish the instruments. So, the system enables the robot to track the usual instruments used in laparoscopic operations. Since the instruments are normally the most structured objects in laparoscopic scenes, the algorithm uses the Hough transform to detect straight lines in the scene. In order to distinguish among different instruments or other structured elements present in the scene, motion information is also used. We give in this paper a detailed description of all stages of the system

Estudio del linaje poseedor de La hacienda Santa Eulalia. Desde el s. XVI al S. XX

Quasi cinc-cents anys d'història d'unes terres dites "la hacienda Santa Eulalia" a la població de Sax, a l'Alt Vinalopó (Alacant). Al s. XIX es convertirien en "La Colònia de Santa Eulalia", (agrícola) emmirallada a les Colònies Tèxtils del les ribes del Llobregat, pertanyent a la mateixa família per entroncaments del Capità Rodríguez de Navarra a la família Saavedra, Comtes d'Alcúdi

Patio de la Alhambra

Siglo XI

Porte de la Justice. Entrée de l'Alhambra

Siglo XI

Cour des Ambassadeurs. Alhambra de Granade

Siglo XI