Abstract
In this thesis, we introduced the problem of privacy-preserving speech processing through a few applications: speaker verification, speaker identification, and speech recognition. There are, however, many problems in speech processing where the similar techniques can be adapted to. Additionally, there are other algorithmic improvements that will allow us to create more accurate or more efficient privacy-preserving solutions for these problems. We discuss a few directions for future research below.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Gentry C (2009) Fully homomorphic encryption using ideal lattices. In: ACM symposium on theory of computing, pp 169–178
Gentry C (2010a) Toward basing fully homomorphic encryption on worst-case hardness. In: CRYPTO, pp 116–137
Gentry C (2010b) Computing arbitrary functions of encrypted data. Commun ACM 53(3):97–105
Kearns M, Tan J, Jennifer W (2007) Privacy-preserving belief propagation and sampling. In: Neural information processing systems
Lauter K, Naehrig M, Vaikuntanathan V (2011) Can homomorphic encryption be practical? In: ACM cloud computing security workshop
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Pathak, M.A. (2013). Future Work. In: Privacy-Preserving Machine Learning for Speech Processing. Springer Theses. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4639-2_13
Download citation
DOI: https://doi.org/10.1007/978-1-4614-4639-2_13
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-4638-5
Online ISBN: 978-1-4614-4639-2
eBook Packages: EngineeringEngineering (R0)