DeepAI AI Chat
Log In Sign Up

A Tandem Framework Balancing Privacy and Security for Voice User Interfaces

by   Ranya Aloufi, et al.

Speech synthesis, voice cloning, and voice conversion techniques present severe privacy and security threats to users of voice user interfaces (VUIs). These techniques transform one or more elements of a speech signal, e.g., identity and emotion, while preserving linguistic information. Adversaries may use advanced transformation tools to trigger a spoofing attack using fraudulent biometrics for a legitimate speaker. Conversely, such techniques have been used to generate privacy-transformed speech by suppressing personally identifiable attributes in the voice signals, achieving anonymization. Prior works have studied the security and privacy vectors in parallel, and thus it raises alarm that if a benign user can achieve privacy by a transformation, it also means that a malicious user can break security by bypassing the anti-spoofing mechanism. In this paper, we take a step towards balancing two seemingly conflicting requirements: security and privacy. It remains unclear what the vulnerabilities in one domain imply for the other, and what dynamic interactions exist between them. A better understanding of these aspects is crucial for assessing and mitigating vulnerabilities inherent with VUIs and building effective defenses. In this paper,(i) we investigate the applicability of the current voice anonymization methods by deploying a tandem framework that jointly combines anti-spoofing and authentication models, and evaluate the performance of these methods;(ii) examining analytical and empirical evidence, we reveal a duality between the two mechanisms as they offer different ways to achieve the same objective, and we show that leveraging one vector significantly amplifies the effectiveness of the other;(iii) we demonstrate that to effectively defend from potential attacks against VUIs, it is necessary to investigate the attacks from multiple complementary perspectives(security and privacy).


page 1

page 2

page 3

page 4


Locally Authenticated Privacy-preserving Voice Input

Increasing use of our biometrics (e.g., fingerprints, faces, or voices) ...

A Practical Guide to Logical Access Voice Presentation Attack Detection

Voice-based human-machine interfaces with an automatic speaker verificat...

Evaluating Voice Conversion-based Privacy Protection against Informed Attackers

Speech signals are a rich source of speaker-related information includin...

Voice Privacy with Smart Digital Assistants in Educational Settings

The emergence of voice-assistant devices ushers in delightful user exper...

Practical Attacks on Voice Spoofing Countermeasures

Voice authentication has become an integral part in security-critical op...

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques

The growing use of voice user interfaces has led to a surge in the colle...

On the invertibility of a voice privacy system using embedding alignement

This paper explores various attack scenarios on a voice anonymization sy...