Fast and Accurate Likelihood Ratio Based Biometric Comparison in the Encrypted Domain
This addresses privacy threats for users in biometric applications by offering a practical trade-off between efficiency and accuracy, though it is incremental as it builds on existing secure verification methods.
The paper tackles the problem of privacy infringement in biometric verification by proposing a secure system that performs verification in the encrypted domain using elliptic curve homomorphic ElGamal encryption and a log-likelihood ratio classifier, achieving highly accurate results computed within milliseconds.
As applications of biometric verification proliferate, users become more vulnerable to privacy infringement. Biometric data is very privacy sensitive as it may contain information as gender, ethnicity and health conditions which should not be shared with third parties during the verification process. Moreover, biometric data that has fallen into the wrong hands often leads to identity theft. Secure biometric verification schemes try to overcome such privacy threats. Unfortunately, existing secure solutions either introduce a heavy computational or communication overhead or have to accept a high loss in accuracy; both of which make them impractical in real-world settings. This paper presents a novel approach to secure biometric verification aiming at a practical trade-off between efficiency and accuracy, while guaranteeing full security against honest-but-curious adversaries. The system performs verification in the encrypted domain using elliptic curve based homomorphic ElGamal encryption for high efficiency. Classification is based on a log-likelihood ratio classifier which has proven to be very accurate. No private information is leaked during the verification process using a two-party secure protocol. Initial tests show highly accurate results that have been computed within milliseconds range.