By Guillermo Navarro-Arribas, Vicenç Torra
This booklet offers an summary of the study paintings on facts privateness and privateness improving applied sciences carried through the individuals of the ARES undertaking. ARES (Advanced examine in privateness an safety, CSD2007-00004) has been probably the most very important examine initiatives funded by means of the Spanish govt within the fields of computing device defense and privateness. it truly 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 examine teams. The undertaking begun in 2007 and may end this 2014. Composed via 6 study teams from 6 various associations, it has accumulated a huge variety of researchers in the course of its lifetime.
Among the paintings produced by way of the ARES venture, one particular paintings package deal has been regarding privateness. This books gathers works produced via individuals of the undertaking concerning facts privateness and privateness bettering applied sciences. The awarded works not just summarize vital examine carried within the venture but in addition function an summary of the cutting-edge in present study on information privateness and privateness improving technologies.
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Additional info for Advanced Research in Data Privacy
Chapter 9, Advanced Research on Data Privacy. Springer, Berlin (2014) 48. : On the protection of social networks user’s information. -Based Syst. 49, 134–144 (2013) 49. : Approximating degree sequences with regular graphic sequences, manuscript (2014) 50. : A Summary of k-Degree Anonymous Methods for Privacy-Preserving on Networks. Chapter 13, Advanced Research on Data Privacy. Springer, Berlin (2014) 51. : Data protection for online social networks and p-stability for graphs, manuscript (2014) 52.
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. 489–494 (2009) 36 V. Torra and G. Navarro-Arribas 43. : A Face Is Exposed for AOL Searcher No.
1 Introduction Data privacy studies how to protect the privacy of individuals and corporations. The areas of Privacy Preserving Data Mining (PPDM) and Statistical Disclosure Control (SDC) study the theory, tools, and methodologies with this objective. Data privacy methods and technologies can be classified according to different dimensions. , respondent, owner and user privacy). 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).