On the role of contextual information for privacy attacks and classification

CVRČEK, Daniel a 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, s. 31-39.

Základní údaje

Originální název

On the role of contextual information for privacy attacks and classification

Název česky

O úloze konextových informací pro útoky proti a klasifikaci soukromí

Autoři

CVRČEK, Daniel a Václav MATYÁŠ ML. ORCID

Vydání

Los Alamitos, Proceedings of the 2004 IEEE International Conference on Data Mining, Workshop on Privacy and Security Aspects of Data Mining, od s. 31-39, 9 s. 2004

Nakladatel

IEEE Computer Society

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Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

Contextual Information; Privacy; Attacks; Classification

Štítky

Attacks, CLASSIFICATION, Contextual Information, privacy

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

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.

Česky

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.