Ph.D. Electrical and Computer Engineering, 2017 - Present
University of Wisconsin-Madison
M.S. Electrical and Computer Engineering, 2017
Ain Shams University, Cairo, Egypt
I am a Ph.D. student at the University of Wisconsin-Madison, Electrical and Computer Engineering Department. My research interests lie at the intersection of Privacy, Systems, Signal Processing, and Machine Learning. Currently, I am advised by Kassem Fawaz working on the design of privacy enhancing systems for emerging technologies. Previously, I obtained my M.Sc. degree from my alma mater Ain Shams University, Cairo, Egypt in 2017. My background spans different topics including wireless communications, signal processing, and Analog and RF IC design.
In this project, we study and quantify the utility-privacy trade-offs between online (Cloud-based) and offline speech transcription services. Cloud-based transcription services pose serious privacy threats as speech is a rich source of sensitive acoustic and textual information. Although offline systems eliminates the privacy risks, we found that its transcription performance is inferior to that of cloud-based systems, especially for real-world use cases.
In this project, we propose Pr$\epsilon\epsilon$ch, an end-to-end speech transcription system which lies at an intermediate point in the privacy-utility spectrum. It relies on cloud-based services to transcribe a speech file after applying a series of privacy-preserving operations on the user’s side. These operations protect the acoustic features of the speakers’ voices and protect the privacy of the textual content at an improved performance relative to offline system. Additionally, Pr$\epsilon\epsilon$ch provides several control knobs to allow customizable utility-usability-privacy trade-off. We perform a comprehensive evaluation of Pr$\epsilon\epsilon$ch, using diverse real-world datasets, that demonstrates its effectiveness. Pr$\epsilon\epsilon$ch provides transcriptions at a 2% to 32.25% (mean 17.34%) relative improvement in word error rate over Deep Speech, while fully obfuscating the speakers' voice biometrics and allowing only a differentially private view of the textual content. A demo of Pr$\epsilon\epsilon$ch can be found here.
Shimaa Ahmed, Amrita Roy Chowdhury, Kassem Fawaz, and Parmesh Ramanathan, 2020, February. Preech: A System for Privacy-Preserving Speech Transcription. [Pdf]