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ICASSP 2019 Poster - Privacy-preserving Paralinguistic Tasks

Citation Author(s):
Francisco Teixeira, Alberto Abad, Isabel Trancoso
Submitted by:
Francisco Teixeira
Last updated:
10 May 2019 - 10:21am
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Francisco Teixeira
Paper Code:
3713
 

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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