On the role of contextual information for privacy attacks and classification

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.

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

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Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Organization unit

Faculty of Informatics

Keywords in English

Contextual Information; Privacy; Attacks; Classification

Tags

Attacks, CLASSIFICATION, Contextual Information, privacy

Tags

International impact, Reviewed

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Abstract

V originále

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.

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.