Privacy-Enhancing Technique for Eye Tracking in Mixed Reality
We are seeking to bridge the gap of user privacy in human-centered sensing. Eye tracking enables a more immersive experience for today’s user interface, while its rich information is exposed, inducing privacy concerns about users’ interest, health, identity, etc. Through the on-going project, we are exploring the opportunity to design real-time privacy control knob for eye-tracking in mixed-reality.
Pr$\epsilon\epsilon$ch: A System for Privacy-Preserving Speech Transcription
Speech Transcription has become a commercially mature technology that automates the transcription of continuous speech. Cloud services possess highly accurate and scalable speech transcription models as a part of the machine learning as a service (MLaaS) business model. This service is very convenient for many applications such as journalism, education, conference meetings, and health care documentation. However, speech is a rich source of sensitive acoustic and textual information; hence, using cloud services poses significant privacy threats to the users. 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 the offline system, Deep Speech. Additionally, Pr$\epsilon\epsilon$ch provides several control knobs to customize the utility-usability-privacy trade-off. We perform a comprehensive evaluation of Pr$\epsilon\epsilon$ch, using diverse real-world use cases, 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. Preech: A system for privacy-preserving speech transcription. In 29th USENIX Security Symposium (USENIX Security 20), pages 2703–2720. USENIX Association, Aug. 2020 [Pdf] [Slides] [USENIX Talk]
Project Velody: Resilient User Authentication by Nonlinear Vibration
We developed a novel solution, Velody, for user authentication in smart homes using unique vibration responses from users’ hands. Thanks to the nonlinear properties in hand-surface vibration, Velody can generate a large number of secret keys resilient to various attacks and accurately authenticate users in less than one second. This work is presented at ACM CCS 2019.
Li, Jingjie, Fawaz, Kassem and Kim, Younghyun, 2019, November. Velody: Nonlinear Vibration Challenge-Response for Resilient User Authentication. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 1201-1213).
Face-Off: Adversarial Face Obfuscation
To help prevent users' faces from being recognized with deep learning, we hide faces by applying imperceptible adversarial perturbations to uploaded faces. Face-Off uses the transferability property of adversarial examples to cause faces uploaded to proprietary face recognition platforms to be mislabeled. This way, users can still use face detection services, but do not have to be worried about their faces being associated with locations and activites, nor do users need to worry about their faces being scraped for training data.
A high-level overview of Face-Off’s system.