POSTER: Secure authentication from facial attributeswith no privacy loss

Authors:
Orr Dunkelman;Margarita Osadchy;Mahmood Sharif
Affiliations:
University of Haifa, Haifa, Israel;University of Haifa, Haifa, Israel;University of Haifa, Haifa, Israel
Venue:
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Year:
2013

Citing 11
Cited 0

A fuzzy commitment scheme

CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Error-tolerant password recovery

CCS '01 Proceedings of the 8th ACM conference on Computer and Communications Security
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Face Recognition with Renewable and Privacy Preserving Binary Templates

AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs

IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of BioHashing and its variants

Pattern Recognition
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data

SIAM Journal on Computing
The practical subtleties of biometric key generation

SS'08 Proceedings of the 17th conference on Security symposium
A hybrid approach for generating secure and discriminating face template

IEEE Transactions on Information Forensics and Security
Protecting Biometric Templates With Sketch: Theory and Practice

IEEE Transactions on Information Forensics and Security - Part 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

Biometric authentication is more secure than using regular passwords, as biometrics cannot be "forgotten" and contain high entropy. Thus, many constructions rely on biometric features for authentication, and use them as a source for "good" cryptographic keys. At the same time, biometric systems carry with them many privacy concerns. We describe a proof-of-concept (PoC) which transforms facial attributes from a single image into keys in a consistent, discriminative, and privacy-aware manner. The outcome is a user-specific string that cannot be guessed, and it reveals no information concerning the users of the system, even when the system's secrets are revealed.