Patient Privacy in Paralinguistic Tasks

Francisco Teixeira, Alberto Abad, Isabel Trancoso

Recent developments in cryptography and, in particular in Fully Homomorphic Encryption (FHE), have allowed for the development of new privacy preserving machine learning schemes. In this paper, we show how these schemes can be applied to the automatic assessment of speech affected by medical conditions, allowing for patient privacy in diagnosis and monitoring scenarios. More specifically, we present results for the assessment of the degree of Parkinson’s Disease, the detection of a Cold and both the detection and assessment of the degree of Depression. To this end, we use a neural network in which all operations are performed in an FHE context. This implies replacing the activation functions by linear and second degree polynomials, as only additions and multiplications are viable. Furthermore, to guarantee that the inputs of these activation functions fall within the convergence interval of the approximation, a batch normalization layer is introduced before each activation function. After training the network with unencrypted data, the resulting model is then employed in an encrypted version of the network, to produce encrypted predictions. Our tests show that the use of this framework yields results with little to no performance degradation, in comparison to the baselines produced for the same datasets.

 DOI: 10.21437/Interspeech.2018-2186

Cite as: Teixeira, F., Abad, A., Trancoso, I. (2018) Patient Privacy in Paralinguistic Tasks. Proc. Interspeech 2018, 3428-3432, DOI: 10.21437/Interspeech.2018-2186.

  author={Francisco Teixeira and Alberto Abad and Isabel Trancoso},
  title={Patient Privacy in Paralinguistic Tasks},
  booktitle={Proc. Interspeech 2018},