Title: Privacy-Preserving Machine Learning for Speech Processing
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.
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, Speech processing systems, Automatic speech recognition, Machine learning, Computer security