Privacy-preserving Paralinguistic Tasks
Francisco Teixeira, Alberto Abad, Isabel Trancoso
Abstract
Speech is one of the primary means of communication for humans. It can be viewed as a carrier for information on several levels as it conveys not only the meaning and intention predetermined by a speaker, but also paralinguistic and extralinguistic information about the speaker's age, gender, personality, emotional state, health state and affect. This makes it a particularly sensitive biometric, that should be protected. In this work we intent to explore how Leveled Homomorphic Encryption can be combined with a Neural Network to create a privacy-preserving machine learning framework for speech-based health-related tasks. In particular, we will apply this framework to the detection and assessment of a Cold, Depression and Parkinson's Disease. Moreover, we will show how using a Quantized Neural Network, with discretized weights, allows us to apply a Leveled Homomorphic Encryption technique called batching that can be utilized to reduce the effective computational cost of this framework.
BibTeX
@inproceedings{icassp2019_privacypreservin,
title = {Privacy-preserving Paralinguistic Tasks},
author = {Francisco Teixeira and Alberto Abad and Isabel Trancoso},
booktitle = {ICASSP 2019},
year = {2019}
}