Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network

Personalized recommender systems rely on each user's personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users' rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.Comment: The new updated version of the pape

Statistical disclosure control in tabular data

Data disseminated by National Statistical Agencies (NSAs) can be classified as either microdata or tabular data. Tabular data is obtained from microdata by crossing one or more categorical variables. Although cell tables provide aggregated information, they also need to be protected. This chapter is a short introduction to tabular data protection. It contains three main sections. The first one shows the different types of tables that can be obtained, and how they are modeled. The second describes the practical rules for detection of sensitive cells that are used by NSAs. Finally, an overview of protection methods is provided, with a particular focus on two of them: “cell suppression problem” and “controlled tabular adjustment”.Postprint (published version

Fast Generation of Accurate Synthetic Microdata

Generation of a synthetic microdata set that reproduces the statistical properties of an original microdata set is a promising approach to statistical disclosure control (SDC) of microdata. In this paper, a new method for generating continuous synthetic microdata is proposed. The covariance matrix and the univariate statistics of the original data set are exactly preserved. The method is non-iterative and its complexity grows linearly with the number of records to be protected

Outlier Protection in Continuous Microdata Masking

Masking methods protect data sets against disclosure by perturbing the original values before publication. Masking causes some information loss (masked data are not exactly the same as original data) and does not completely suppress the risk of disclosure for the individuals behind the data set. Information loss can be measured by observing the di#erences between original and masked data while disclosure risk can be measured by means of record linkage and confidentiality intervals

Detecção do provírus da Imunodeficiência Felina em gatos domésticos pela técnica de Reação em Cadeia da Polimerase Detection of feline immunodeficiency provirus in domestic cats by polymerase chain reaction

A infecção de gatos domésticos pelo Vírus da Imunodeficiência Felina (FIV) é um dos modelos mais promissores para o estudo da infecção pelo vírus da imunodeficiência humana (HIV) que causa a Síndrome de Imunodeficiência Adquirida (AIDS). O FIV causa, em gatos, uma enfermidade similar àquela observada em pacientes com AIDS, sobretudo no que diz respeito ao aumento da susceptibilidade a infecções oportunistas. No presente estudo, utilizou-se a Reação em Cadeia da Polimerase (PCR), com o objetivo de detectar o provírus do FIV em gatos com sinais clínicos de imunodeficiência. O fragmento de DNA escolhido como alvo para amplificação situa-se no gene gag do lentivírus felino, o qual é conservado entre as diferentes amostras do vírus. O DNA utilizado foi extraído a partir de amostras de sangue e de tecidos de animais com suspeita clínica de imunodeficiência. Das 40 amostras analisadas, 15 foram positivas, das quais 4 foram submetidas à hibridização, confirmando a especificidade dos fragmentos amplificados. Esses resultados demonstram a presença do FIV na população de gatos domésticos do Rio Grande do Sul, Brasil.<br>Feline immunodeficiency virus (FIV) infection of domestic cats is one of the most promising animal models for the infection by the human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS). Infected cats may develop a disease similar to that observed in AIDS patients, with increased susceptibility to opportunistic infections. In this study we used the polymerase chain reaction (PCR) to detect proviral DNA of feline immunodeficiency virus on the blood and tissue samples from cats with a clinical diagnosis of immunodeficiency. The PCR primers were used to amplify the gag gene, which is conserved among different isolates. From 40 samples analyzed, 15 were positive and 4 of them were submitted to hybridization to confirm the specificity of the amplified fragments. These results confirm the presence of FIV in domestic cats in Rio Grande do Sul, Brazil