Privacy-Preserving Machine Learning for Speech Processing
By: by Manas A. Pathak
Contributors: SpringerLink (Online service)
Series: Springer Theses, Recognizing Outstanding Ph.D. Research
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 141
ISBN-13: 9781461446392
Subjects: Cryptology and Information Theory Data Structures; Telecommunication; Engineering; Data structures (Computer science); Networks Communications Engineering; Production of electric energy or power; Electrical Machines and Networks Power Electronics; Image and Speech Processing Signal
Description: This thesis discusses the privacy issues in speech-based applications, including biometric authentication, surveillance, and external speech processing services. Manas A. Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification, and speech recognition. <br><br>The thesis introduces tools from cryptography and machine learning and current techniques for improving the efficiency and scalability of the presented solutions, as well as experiments with prototype implementations of the solutions for execution time and accuracy on standardized speech datasets. Using the framework proposed  may make it possible for a surveillance agency to listen for a known terrorist, without being able to hear conversation from non-targeted, innocent civilians.
Table of contents: title: Thesis Overview; title: Speech Processing Background; title: Privacy Background; title: Overview of Speaker Verification with Privacy; title: Privacy-Preserving Speaker Verification Using Gaussian Mixture Models; title: Privacy-Preserving Speaker Verification as String Comparison; title: Overview of Speaker Indentification with Privacy; title: Privacy-Preserving Speaker Identification Using Gausian Mixture Models; title: Privacy-Preserving Speaker Identification as String Comparison; title: Overview of Speech Recognition with Privacy; title: Privacy-Preserving Isolated-Word Recognition; title: Thesis Conclusion; title: Future Work; title: Differentially Private Gaussian Mixture Models.