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 (203 Česká republika) a Václav MATYÁŠ ML. (203 Česká republika, garant).
• 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

Další údaje

• Originální 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

Anotace

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.

Anotace 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.