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Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies

Terhörst, Philipp ; Huber, Marco ; Damer, Naser ; Rot, Peter ; Kirchbuchner, Florian ; Struc, Vitomir ; Kuijper, Arjan (2020)
Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies.
19th International Conference of the Biometrics Special Interest Group (BIOSIG 2020). virtual Conference (16.-18.09.)
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2020
Autor(en): Terhörst, Philipp ; Huber, Marco ; Damer, Naser ; Rot, Peter ; Kirchbuchner, Florian ; Struc, Vitomir ; Kuijper, Arjan
Art des Eintrags: Bibliographie
Titel: Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies
Sprache: Englisch
Publikationsjahr: 2020
Veranstaltungstitel: 19th International Conference of the Biometrics Special Interest Group (BIOSIG 2020)
Veranstaltungsort: virtual Conference
Veranstaltungsdatum: 16.-18.09.
URL / URN: https://dl.gi.de/handle/20.500.12116/34330
Kurzbeschreibung (Abstract):

Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.

Freie Schlagworte: Biometrics, Face recognition, Privacy enhancing technologies, Evaluation schemes, Machine learning
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing
Hinterlegungsdatum: 29 Sep 2020 07:49
Letzte Änderung: 27 Feb 2023 11:25
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