By Guillermo Navarro-Arribas, Vicenç Torra
This publication offers an summary of the study paintings on info privateness and privateness bettering applied sciences carried through the contributors of the ARES venture. ARES (Advanced study in privateness an defense, CSD2007-00004) has been some of the most vital learn initiatives funded by way of the Spanish govt within the fields of computing device safeguard and privateness. it really is a part of the now extinct CONSOLIDER INGENIO 2010 application, a hugely aggressive application which aimed to improve wisdom and open new learn strains between most sensible Spanish study teams. The undertaking begun in 2007 and may end this 2014. Composed by means of 6 examine teams from 6 diversified associations, it has collected a big variety of researchers in the course of its lifetime.
Among the paintings produced by means of the ARES undertaking, one particular paintings package deal has been regarding privateness. This books gathers works produced via contributors of the venture regarding facts privateness and privateness improving applied sciences. The awarded works not just summarize very important study carried within the undertaking but additionally function an summary of the state-of-the-art in present learn on facts privateness and privateness improving technologies.
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Additional info for Advanced Research in Data Privacy
Lect. Notes Comput. Sci. 4259, 899–908 (2006) 39. : Distance based re-identification for time series. Analysis of distances. Lect. Notes Comput. Sci. 4302, 205–216 (2006) 40. : A similarity measure for sequences of categorical data based on the ordering of common elements. LNAI 5285, 134–145 (2008) 41. : Generation of prototypes for masking sequences of events. In: Proceedings ARES 2009, WAIS Workshop, pp. 947–952 (2009) 42. : On the use of aggregation operators for location privacy. In: Proceedings IFSA-EUSFLAT, pp.
13 in this book ). We have also explored  the relationship between graphs and the concept of p-stability. Once a privacy model is selected, we have explored tools as clustering , edge modification , as well as other perturbation techniques [22, 54] to achieve a protected version of the social network. 2 Information Loss Measures We have defined information loss measures and used them for various types of data. In particular, we have considered measures based on clustering, classification and probability distributions.
For example, in numerical  and categorical  data for files, logs , and graphs for social networks . Classification based measures in  showed that masked data is still useful for building classifiers. Hellinger distance between probability distributions and entropy have been considered in relation to categorical data . 3 Disclosure Risk Measures We have studied measures of disclosure when they are defined in terms of reidentification. That is, the measure is proportional to the number of links between intruders data and released data.
Advanced Research in Data Privacy by Guillermo Navarro-Arribas, Vicenç Torra