Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples. (arXiv:2211.05446v1 [cs.SD])

Faced with the threat of identity leakage during voice data publishing, users
are engaged in a privacy-utility dilemma when enjoying convenient voice
services. Existing studies employ direct modification or text-based
re-synthesis to de-identify users' voices, but resulting in inconsistent
audibility in the presence of human participants. In this paper, we propose a
voice de-identification system, which uses adversarial examples to balance the
privacy and utility of voice services. Instead of typical additive examples
inducing perceivable distortions, we design a novel …

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