Advanced Research in Data Privacy (Studies in Computational Intelligence)
Language: English
Pages: 463
ISBN: 3319098845
Format: PDF / Kindle (mobi) / ePub
This book provides an overview of the research work on data privacy and privacy enhancing technologies carried by the participants of the ARES project. ARES (Advanced Research in Privacy an Security, CSD2007-00004) has been one of the most important research projects funded by the Spanish Government in the fields of computer security and privacy. It is part of the now extinct CONSOLIDER INGENIO 2010 program, a highly competitive program which aimed to advance knowledge and open new research lines among top Spanish research groups. The project started in 2007 and will finish this 2014. Composed by 6 research groups from 6 different institutions, it has gathered an important number of researchers during its lifetime.
Among the work produced by the ARES project, one specific work package has been related to privacy. This books gathers works produced by members of the project related to data privacy and privacy enhancing technologies. The presented works not only summarize important research carried in the project but also serve as an overview of the state of the art in current research on data privacy and privacy enhancing technologies.
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’13, To appear (2013) 12. Reiter, M., Rubin, A.: Crowds: anonymity for web transactions. ACM Trans. Inf. Syst. Secur. 1, 66–92 (1998)CrossRef 13. Viejo, A., Castellà-Roca, J.: Using social networks to distort users’ profiles generated by web search engines. Comput. Netw. 54, 1343–1357 (2010)CrossRefMATH 14. Wright, M.K., Adler, M., Levine, B.N., Shields, C.: The predecessor attack: an analysis of a threat to anonymous communications systems. ACM Trans. Inf. Syst. Secur. 7, 489–522 (2004).
(projects COPRIVACY (TIN2011-27076-C03-03), N-KHRONOUS (TIN2010-15764), and ARES (CONSOLIDER INGENIO 2010 CSD2007-00004)) and by the EC (FP7/2007-2013) Data without Boundaries (grant agreement number 262608) is acknowledged. The work contributed by the first author was carried out as part of the Computer Science Ph.D. program of the Universitat Autónoma de Barcelona (UAB). References 1. Abril, D., Navarro-Arribas, G., Torra, V.: Supervised learning using mahalanobis distance for record.
Knowledge structure exploited to guide that assessment. In this work, the use of semantic similarities to guide the linkage process produced a higher number of correct linkages than the standard non-semantic approach (which is just based on comparing value labels), thus providing a more realistic evaluation of disclosure risk. In [44, 51], the semantic RL method was also used to evaluate the practical privacy of the semantically-grounded protection mechanisms discussed above. In [63] risk.
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Rolando Trujillo-Rasua1 and Josep Domingo-Ferrer2 (1)Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Walferdange, Luxembourg (2)Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Catalonia, Spain Rolando Trujillo-Rasua (Corresponding author) Email: rolandotrujillo@uni.lu Josep Domingo-Ferrer Email: josepdomingo@urv.cat Abstract Technologies able to track moving objects such as GPS, GSM, and RFID, have been.