By Guillermo Navarro-Arribas, Vicenç Torra
This booklet offers an summary of the examine paintings on information privateness and privateness improving applied sciences carried through the contributors of the ARES venture. ARES (Advanced examine in privateness an safety, CSD2007-00004) has been essentially the most very important study initiatives funded by means of the Spanish executive within the fields of machine protection and privateness. it's a part of the now extinct CONSOLIDER INGENIO 2010 software, a hugely aggressive application which aimed to strengthen wisdom and open new study traces between most sensible Spanish examine teams. The undertaking all started in 2007 and may end this 2014. Composed by way of 6 learn teams from 6 various associations, it has collected a tremendous variety of researchers in the course of its lifetime.
Among the paintings produced by way of the ARES undertaking, one particular paintings package deal has been regarding privateness. This books gathers works produced via individuals of the venture relating to facts privateness and privateness bettering applied sciences. The provided works not just summarize very important study carried within the undertaking but additionally function an summary of the cutting-edge in present examine on information privateness and privateness bettering technologies.
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Additional info for Advanced Research in Data Privacy
In this chapter, firstly we review the underlying scenario used in statistical disclosure control (SDC) and Privacy-Preserving Data Mining (PPDM). In this chapter, we describe the attribute disclosure underlying scenario, the different forms of background knowledge of the adversary the adversary may have and their potential privacy attacks. then, we review the approaches introduced in the literature to tackle attribute disclosure attacks. 1 The Underlying Scenario We now proceed with a more formal description of the scenario that guides our presentation.
The authors mine negative association rules from the data as the background knowledge of the adversary and then anonymize the data in such a way that eliminates this knowledge from the data. The idea is that the background knowledge of the adversary reflects itself in the data, therefore, data mining approaches should be able to extract this knowledge. Although in this technique only the negative association rules are considered as the background knowledge of the adversary, Li et al talk about the possibility of discovering the other types of background knowledge from the data as long as it does not have any negative effect on the utility of data .
Another is on our knowledge of the type of computation a third party is interested to compute with the data (either we know what will be done or we do not know). See [1, 2] for a detailed description of the classification, and a survey on these areas. See also the following texts and monographies [3–7]. V. org G. cat © Springer International Publishing Switzerland 2015 G. Navarro-Arribas and V. 1007/978-3-319-09885-2_3 27 28 V. Torra and G. Navarro-Arribas In this chapter we present the main results of our group in the field of data privacy organized according to the previous classification about whose privacy is being protected.
Advanced Research in Data Privacy by Guillermo Navarro-Arribas, Vicenç Torra