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Privacy-preserving Paralinguistic Tasks


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.

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Paper Details

Alberto Abad, Isabel Trancoso
Submitted On:
10 May 2019 - 9:54am
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Presenter's Name:
Francisco Teixeira
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Privacy-preserving Paralinguistic Tasks Poster



[1] Alberto Abad, Isabel Trancoso, "Privacy-preserving Paralinguistic Tasks", IEEE SigPort, 2019. [Online]. Available: Accessed: May. 23, 2019.
url = {},
author = {Alberto Abad; Isabel Trancoso },
publisher = {IEEE SigPort},
title = {Privacy-preserving Paralinguistic Tasks},
year = {2019} }
T1 - Privacy-preserving Paralinguistic Tasks
AU - Alberto Abad; Isabel Trancoso
PY - 2019
PB - IEEE SigPort
UR -
ER -
Alberto Abad, Isabel Trancoso. (2019). Privacy-preserving Paralinguistic Tasks. IEEE SigPort.
Alberto Abad, Isabel Trancoso, 2019. Privacy-preserving Paralinguistic Tasks. Available at:
Alberto Abad, Isabel Trancoso. (2019). "Privacy-preserving Paralinguistic Tasks." Web.
1. Alberto Abad, Isabel Trancoso. Privacy-preserving Paralinguistic Tasks [Internet]. IEEE SigPort; 2019. Available from :