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).

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