  In many voice biometrics applications there is a requirement to preserve
privacy, not least because of the recently enforced General Data Protection
Regulation (GDPR). Though progress in bringing privacy preservation to voice
biometrics is lagging behind developments in other biometrics communities,
recent years have seen rapid progress, with secure computation mechanisms such
as homomorphic encryption being applied successfully to speaker recognition.
Even so, the computational overhead incurred by processing speech data in the
encrypted domain is substantial. While still tolerable for single biometric
comparisons, most state-of-the-art systems perform some form of cohort-based
score normalisation, requiring many thousands of biometric comparisons. The
computational overhead is then prohibitive, meaning that one must accept either
degraded performance (no score normalisation) or potential for privacy
violations. This paper proposes the first computationally feasible approach to
privacy-preserving cohort score normalisation. Our solution is a cohort pruning
scheme based on secure multi-party computation which enables privacy-preserving
score normalisation using probabilistic linear discriminant analysis (PLDA)
comparisons. The solution operates upon binary voice representations. While the
binarisation is lossy in biometric rank-1 performance, it supports
computationally-feasible biometric rank-n comparisons in the encrypted domain.
