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This e-book offers an outline of the study paintings on facts privateness and privateness bettering applied sciences carried by way of the contributors of the ARES undertaking. ARES (Advanced study in privateness an defense, CSD2007-00004) has been some of the most vital examine initiatives funded via the Spanish govt within the fields of laptop defense 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 study traces between best Spanish study teams. The undertaking began in 2007 and should end this 2014. Composed by means of 6 study teams from 6 varied associations, it has amassed an immense 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 on the topic of privateness. This books gathers works produced by means of participants of the venture on the topic of facts privateness and privateness bettering applied sciences. The offered works not just summarize vital study carried within the venture but in addition function an summary of the cutting-edge in present examine on facts privateness and privateness bettering applied sciences.

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Extra resources for Advanced Research in Data Privacy (Studies in Computational Intelligence, Volume 567)

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Microaggregation- and permutation-based anonymization of movement data. Inf. Sci. 208, 55–80 (2012) 17. : Semantic microaggregation for the anonymization of query logs. In: PSD 2010. LNCS, vol. 6344, pp. 127–137 (2010) 18. : Semantic microaggregation for the anonymization of query logs using the open directory project. SORT-Statistics and Operations Research Transactions, pp. 41–58, Special issue (2011) 19. : Exploting social networks to provide privacy in personalized web search. J. Syst. Soft.

6] introduced a language to represent background knowledge of the adversary. Instead of representing the specific content of adversary’s knowledge, this language quantifies the amount of knowledge of the adversary [7]. It is defined as the set of all possible conjunctions of k implications [7]. For example, the knowledge that “if Hannah has the flu, then Charlie also has the flu”, will be represented as t H annah [Disease] = f lu → tCharlie [Disease] = f lu where t X [S] is value of sensitive attribute S for individual X in a record t.

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). 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.

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