Stop bugging me! Evading modern-day wiretapping using adversarial perturbations. Issue 121 (October 2022)

• Record Type: Journal Article
• Authors: Mathov, Yael; Ben Senior, Tal; Shabtai, Asaf; Elovici, Yuval
• Is Part Of: Computers & security. Issue 121(2022)
• Publication Date: 2022-10
• Subjects: Adversarial examples -- Privacy protection; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2022.102841
• Languages: English
• ISSNs: 0167-4048

Abstract

Highlights: Mass surveillance systems can be used to eavesdrop on private VoIP conversations. Adversarial perturbations can be used as a viable privacy protection solution. Several challenges need to be addressed to create a feasible real-world solution. A Teensy-based device is used to obfuscate conversations on five VoIP applications. Abstract: Mass surveillance systems for voice over IP (VoIP) conversations pose a great risk to privacy. These automated systems use learning models to analyze conversations, and calls that involve specific topics are routed to a human agent for further examination. In this study, we present an adversarial-learning-based framework for privacy protection for VoIP conversations. We present a novel method that finds a universal adversarial perturbation (UAP), which, when added to the audio stream, prevents an eavesdropper from automatically detecting the conversation's topic. As shown in our experiments, the UAP is agnostic to the speaker or audio length, and its volume can be changed in real time, as needed. Our real-world solution uses a Teensy microcontroller that acts as an external microphone and adds the UAP to the audio in real time. We examine different speakers, VoIP applications (Skype, Zoom, Slack, Google Meet, and Microsoft Teams), and audio lengths. Our results in the real world suggest that our approach is a feasible solution for privacy protection.