Advanced Research in Data Privacy by Guillermo Navarro-Arribas, Vicenç Torra PDF

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By Guillermo Navarro-Arribas, Vicenç Torra

ISBN-10: 3319098845

ISBN-13: 9783319098845

ISBN-10: 3319098853

ISBN-13: 9783319098852

This e-book offers an summary of the examine paintings on facts privateness and privateness improving applied sciences carried by way of the individuals of the ARES undertaking. ARES (Advanced study in privateness an safeguard, CSD2007-00004) has been the most vital study initiatives funded by way of the Spanish executive within the fields of laptop defense and privateness. it's a part of the now extinct CONSOLIDER INGENIO 2010 application, a hugely aggressive software which aimed to develop wisdom and open new learn traces between most sensible Spanish examine teams. The venture all started in 2007 and may end this 2014. Composed by way of 6 learn teams from 6 varied associations, it has accumulated 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 concerning privateness. This books gathers works produced via participants of the undertaking regarding info privateness and privateness bettering applied sciences. The provided works not just summarize very important study carried within the venture but additionally function an summary of the cutting-edge in present study on facts privateness and privateness bettering technologies.

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Extra resources for Advanced Research in Data Privacy

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Trans. Data Priv. 1(2), 86–104 (2008) 30. : Edit constraints on microaggregation and additive noise. Lect. Notes Comput. Sci. 6549, 1–14 (2011) 31. : A new framework to automate constrained microaggregation. In: Proceedings PAVLAD Workshop in CIKM, pp. 1–8 (2009) 32. : Protection of micro-data subjecto to edit constraints against statistical disclousure. J. Official Stat. 24(2), 229–253 (2008) 33. : MultiRelational k-Anonymity. Proc. ICDE 2007, 1417– 1421 (2007) 34. : MultiRelational k-anonymity.

Therefore, she knows that one of the records in the Table belongs to Bob and she tries to figure out what Bob’s disease is. As she is Bob’s neighbor, she knows that Bob is American and he is 31 years old. She also knows that he is living in the zip code 13053 (the same as herself). e. records 9, 10, 11, or 12, belongs to Bob. As all patients in that equivalence class have cancer, Alice, without any extra effort to re-identify Bob’s record, will infer that Bob has cancer and therefore she will jeopardize his privacy.

Inf. Secur. 11(4), 253–267 (2012) 70. : Kd-trees and the real disclosure risks of large statistical databases. Inf. Fusion 13, 260–273 (2012) 71. : Toward a privacy agent for information retrieval. Int. J. Intel. Syst. 28(6), 606–622 (2013) 72. : Optimisation-Based Study of Data Privacy by Using PRAM. Chapter 21, Advanced Research on Data Privacy. Springer, Berlin (2014) 73. : Towards knowledge intensive data privacy. Data privacy management and autonomous spontaneous security. Lect. Notes Comput.

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Advanced Research in Data Privacy by Guillermo Navarro-Arribas, Vicenç Torra


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