CVRČEK, Daniel and Václav MATYÁŠ ML. On the role of contextual information for privacy attacks and classification. In Proceedings of the 2004 IEEE International Conference on Data Mining, Workshop on Privacy and Security Aspects of Data Mining. Los Alamitos: IEEE Computer Society, 2004, p. 31-39.
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Basic information
Original name On the role of contextual information for privacy attacks and classification
Name in Czech O úloze konextových informací pro útoky proti a klasifikaci soukromí
Authors CVRČEK, Daniel (203 Czech Republic) and Václav MATYÁŠ ML. (203 Czech Republic, guarantor).
Edition Los Alamitos, Proceedings of the 2004 IEEE International Conference on Data Mining, Workshop on Privacy and Security Aspects of Data Mining, p. 31-39, 9 pp. 2004.
Publisher IEEE Computer Society
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Organization unit Faculty of Informatics
Keywords in English Contextual Information; Privacy; Attacks; Classification
Tags Attacks, CLASSIFICATION, Contextual Information, privacy
Tags International impact, Reviewed
Changed by Changed by: prof. RNDr. Václav Matyáš, M.Sc., Ph.D., učo 344. Changed: 25/6/2009 11:32.
Abstract
Many papers and articles attempt to define or even quantify privacy, typically with a major focus on anonymity. A related research exercise in the area of evidence-based trust models for ubiquitous computing environments has given us an impulse to take a closer look at the definition(s) of privacy in the Common Criteria, which we then transcribed in a bit more formal manner. This lead us to a further review of unlinkability, and revision of another semi-formal model allowing for expression of anonymity and unlinkability -- the Freiburg Privacy Diamond. We propose new means of describing (obviously only observable) characteristics of a system to reflect the role of contexts for profiling -- and linking -- users with actions in a system. We believe this approach should allow for evaluating privacy in large data sets.
Abstract (in Czech)
Many papers and articles attempt to define or even quantify privacy, typically with a major focus on anonymity. A related research exercise in the area of evidence-based trust models for ubiquitous computing environments has given us an impulse to take a closer look at the definition(s) of privacy in the Common Criteria, which we then transcribed in a bit more formal manner. This lead us to a further review of unlinkability, and revision of another semi-formal model allowing for expression of anonymity and unlinkability -- the Freiburg Privacy Diamond. We propose new means of describing (obviously only observable) characteristics of a system to reflect the role of contexts for profiling -- and linking -- users with actions in a system. We believe this approach should allow for evaluating privacy in large data sets.
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