Inference Attacks for X-Vector Speaker Anonymization

• URL: http://arxiv.org/abs/2505.08978v1
• Date: Tue, 13 May 2025 21:42:33 GMT
• Title: Inference Attacks for X-Vector Speaker Anonymization
• Authors: Luke Bauer, Wenxuan Bao, Malvika Jadhav, Vincent Bindschaedler,
• Abstract summary: We revisit the privacy-utility tradeoff of x-vector speaker anonymization.<n>We propose a novel inference attack for de-anonymization.
• Score: 5.883323796501865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We revisit the privacy-utility tradeoff of x-vector speaker anonymization. Existing approaches quantify privacy through training complex speaker verification or identification models that are later used as attacks. Instead, we propose a novel inference attack for de-anonymization. Our attack is simple and ML-free yet we show experimentally that it outperforms existing approaches.

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