Semantic similarity measuring between words can be applied to many applications, such as
Artificial Intelligence, Information Processing, Medical Care and Linguistics. In this paper, we …

The current social and economic context increasingly demands open data to improve
scientific research and decision making. However, when published data refer to individual …

Vast amounts of information are daily exchanged and/or released. The sensitive nature of
much of this information creates a serious privacy threat when documents are uncontrollably …

This work is a detailed companion reproducibility paper of the methods and experiments
proposed by Lastra-Díaz and García-Serrano in (2015, 2016)[56–58], which introduces the …

Personal data are of great interest for research but, at the same time, they pose a serious
privacy risk. Therefore, appropriate data protection measures should be undertaken by the …

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 …

Data privacy is now a hot topic. Big data have increased its importance. Nevertheless,
computational methods for data privacy have been studied and developed since the 70s, at …

Semantic similarity has become, in recent years, the backbone of numerous knowledge-
based applications dealing with textual data. From the different methods and paradigms …

Noise addition is a data distortion technique widely used in data intensive applications. For
example, in machine learning tasks it helps to reduce overfitting, whereas in data privacy …

Users of web search engines are increasingly worried that their query activities may expose
what topics they are interested in, and in turn, compromise their privacy. It would be …