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      Privacy-Preserving Population-Enhanced Biometric Key Generation from Free-Text Keystroke Dynamics

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          Abstract

          Biometric key generation techniques are used to reliably generate cryptographic material from biometric signals. Existing constructions require users to perform a particular activity (e.g., type or say a password, or provide a handwritten signature), and are therefore not suitable for generating keys continuously. In this paper we present a new technique for biometric key generation from free-text keystroke dynamics. This is the first technique suitable for continuous key generation. Our approach is based on a scaled parity code for key generation (and subsequent key reconstruction), and can be augmented with the use of population data to improve security and reduce key reconstruction error. In particular, we rely on linear discriminant analysis (LDA) to obtain a better representation of discriminable biometric signals. To update the LDA matrix without disclosing user's biometric information, we design a provably secure privacy-preserving protocol (PP-LDA) based on homomorphic encryption. Our biometric key generation with PP-LDA was evaluated on a dataset of 486 users. We report equal error rate around 5% when using LDA, and below 7% without LDA.

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          Author and article information

          Journal
          15 July 2014
          Article
          1407.4179
          d735f15e-7160-443f-81c2-8df604275821

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Jaroslav Sedenka, Kiran Balagani, Vir Phoha and Paolo Gasti. Privacy-Preserving Population-Enhanced Biometric Key Generation from Free-Text Keystroke Dynamics. BTAS 2013
          cs.CR

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