Cryptology ePrint Archive: Report 2019/976

Towards real-time hidden speaker recognition by means of fully homomorphic encryption

Martin Zuber and Sergiu Carpov and Renaud Sirdey

Abstract: Securing Neural Network (NN) computations through the use of Fully Homomorphic Encryption (FHE) is the subject of a growing interest in both communities. Among different possible approaches to that topic, our work focuses on applying FHE to hide the model of a neural network-based system in the case of a plain input. In this paper, using the TFHE homomorphic encryption scheme, we propose an efficient fully homomorphic method for an argmin computation on an arbitrary number of encrypted inputs and an asymptotically faster - though levelled - equivalent scheme. Using these schemes and a unifying framework for LWE-based homomorphic encryption schemes (Chimera), we implement a very time-wise efficient, homomorphic speaker recognition scheme using the neural-based embedding system VGGVox. This work can be generalized to all other similar Euclidean embedding-based recognition systems. While maintaining the best-of-class classification rate of the VGGVox system, we implement a speaker-recognition system that can classify a speech sample as coming from one of a 100 hidden model speakers in less than one second.

Category / Keywords: applications / Fully Homomorphic Encryption; Neural Networks; LWE; Nearest Neighbour problem; embedding-based recognition systems; speaker-recognition; FHE performance

Date: received 27 Aug 2019, last revised 29 Aug 2019

Contact author: zubersmartin at gmail com, sergiu carpov@cea fr, renaud sirdey@cea fr

Available format(s): PDF | BibTeX Citation

Version: 20190829:145413 (All versions of this report)

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