This publication offers an summary of the examine paintings on facts privateness and privateness improving applied sciences carried via the individuals of the ARES undertaking. ARES (Advanced learn in privateness an protection, CSD2007-00004) has been probably the most very important examine initiatives funded by means of the Spanish executive within the fields of machine safety and privateness. it truly is a part of the now extinct CONSOLIDER INGENIO 2010 software, a hugely aggressive application which aimed to develop wisdom and open new study traces between best Spanish examine teams. The undertaking begun in 2007 and should end this 2014. Composed by way of 6 examine teams from 6 various associations, it has accumulated a major variety of researchers in the course of its lifetime.
Among the paintings produced via the ARES venture, one particular paintings package deal has been on the topic of privateness. This books gathers works produced by way of participants of the venture with regards to facts privateness and privateness bettering applied sciences. The offered works not just summarize very important study carried within the venture but additionally function an outline of the state-of-the-art in present study on information privateness and privateness bettering applied sciences.
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Extra resources for Advanced Research in Data Privacy (Studies in Computational Intelligence, Volume 567)
In particular, we have considered databases (either as simple numerical  or categorical  tables, or linked tables ), time series  and location privacy , logs (both search  and access [18, 19] logs), documents , and graphs and social networks [21–23]. We give details below for some of these data types. 1 Data Bases Files and Tables We have considered the case of numerical and categorical data. In this area, following [9, 24] and others we have considered data privacy as a multicriteria optimization problem.
In: XII Reunión Española sobre Criptología y Seguridad de la Información, pp. 273–278 (2012) 9. : Preserving users’ privacy in web search engines. Comput. Commun. 32(13), 1541–1551 (2009) 10. : Peer-to-peer private information retrieval. In: PSD 2008. LNCS, vol. 5262, pp. 315–323 (2008) 11. : User-private information retrieval based on a Peer-to-Peer community. Data Knowl. Eng. 68(11), 1237–1252 (2009) 12. : Calibrating noise to sensitivity in private data analysis. In: Proceedings of the 3rd Theory of Cryptography Conference.
This scenario allows third parties to have precise Therefore, we have X nc nc information on confidential data without revealing to whom the confidential data belongs to. Since non-confidential quasi-identifier values are modified when a privacy protection method is applied on data set, valuable knowledge might be lost to favor data owners privacy. A good protection method must achieve a good trade-off between data utility and privacy. In other words, the protected data set X ′ must be: • Close enough to X such that the knowledge and statistics extracted on X ′ will be very similar to those that would be obtained by computing directly on X .
Advanced Research in Data Privacy (Studies in Computational Intelligence, Volume 567)