By Guillermo Navarro-Arribas, Vicenç Torra
This publication presents an outline of the examine paintings on facts privateness and privateness improving applied sciences carried by means of the individuals of the ARES undertaking. ARES (Advanced learn in privateness an protection, CSD2007-00004) has been probably the most vital study initiatives funded by way of the Spanish govt within the fields of desktop safety and privateness. it truly is a part of the now extinct CONSOLIDER INGENIO 2010 software, a hugely aggressive software which aimed to boost wisdom and open new examine traces between most sensible Spanish examine teams. The undertaking all started in 2007 and may end this 2014. Composed by means of 6 study teams from 6 varied associations, it has collected a huge 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 on the topic of privateness. This books gathers works produced by way of participants of the undertaking on the topic of facts privateness and privateness improving applied sciences. The provided works not just summarize vital examine carried within the venture but additionally function an summary of the state-of-the-art in present learn on information privateness and privateness bettering technologies.
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Extra info for Advanced Research in Data Privacy
For instance, consider the data set has also a sensitive attribute Salary and in one of the equivalence classes, containing three records, the values of attribute Salary in those three records are 3, 4, and 5K. If Alice knows that Bob’s record is in this equivalence class, she can infer that Bob’s salary is low since all values of attribute Salary in this equivalence class are in the range [3–5K]. Proximity breach  Generally speaking, a privacy breach on a numerical sensitive attribute occurs even if an adversary can infer a close value to the exact value of the attribute.
Improving record linkage with supervised learning for disclosure risk assessment. Inf. Fusion 13(4), 274–284 (2012) 62. : Supervised learning for record linkage through weighted means and OWA operators. Control Cybern. 39(4), 1011–1026 (2010) 63. : Choquet integral for record linkage. Ann. Oper. Res. 195, 97–110 (2012) 64. : Supervised Learning Using a Symmetric Bilinear Form for Record Linkage, manuscript (2014) 65. : Rethinking rank swapping to decrease disclosure risk. Data Knowl. Eng. 64(1), 346–364 (2008) 66.
29(8), 875–890 (2010) 58. : On the Hellinger distance for measuring information loss in microdata, UNECE / Eurostat Work Session on Statistical Confidentiality, 8th Work Session 2013. Ottawa, Canada (2013) 59. 5022 (2013) 60. : A formalization of record linkage and its application to data protection. Int. J. Unc. Fuzziness Knowl. Based Syst. 20(6), 907–919 (2012) 61. : Improving record linkage with supervised learning for disclosure risk assessment. Inf. Fusion 13(4), 274–284 (2012) 62. : Supervised learning for record linkage through weighted means and OWA operators.