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Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples. (arXiv:2211.05446v1 [cs.SD])
Nov. 11, 2022, 2:20 a.m. | Meng Chen, Li Lu, Jiadi Yu, Yingying Chen, Zhongjie Ba, Feng Lin, Kui Ren
cs.CR updates on arXiv.org arxiv.org
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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