Inference Attacks for X-Vector Speaker Anonymization (2505.08978v1)

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.

An Expert Analysis of Inference Attacks for X-Vector Speaker Anonymization

The paper "Inference Attacks for X-Vector Speaker Anonymization" offers a robust examination of the privacy-utility dynamics in speaker anonymization using x-vectors. The authors propose a novel inference attack method that challenges existing machine learning approaches by exhibiting superior performance in de-anonymizing speakers without the need for complex model training.

Speaker Anonymization and Existing Approaches

Speaker anonymization aims to disguise speaker identity while retaining the intelligibility and expressivity of speech. Traditional approaches rely on x-vector embeddings, which represent speaker-specific phonetic features and are transformed to pseudonymize the speaker. Typically, privacy evaluation methodologies employ complex machine learning models, trained to perform speaker identification or verification tasks, to serve as potential attacks on anonymized speech samples. However, these models may overlook specifics of x-vector transformations, restricting their efficacy.

Proposed Inference Attack

In stark contrast to machine learning-based strategies, this paper introduces a straightforward ML-free inference attack. The attack capitalizes on the x-vector transformation mechanics to infer the original speaker identity. By leveraging the pseudo x-vector's construction details, the attack identifies speakers with remarkable accuracy. The authors validate the robustness of this approach against conventional ML-based attacks, highlighting its computational efficiency and effectiveness without compromising accuracy.

Implications of Numerical Results

The paper presents compelling numerical results (Table1) to substantiate the efficacy of their inference attack, achieving 100% accuracy in scenarios where the original speaker's audio samples are accessible to the attacker. Even when the attacker relies on different utterances from potential speakers, the attack maintains high success rates, significantly outperforming ML-based alternatives.

The attack's performance in open-world settings, where the original speaker may not be in the target pool, further demonstrates its strategic advantage. By establishing a threshold to discern speaker inclusion within the target pool, the technique ensures proficient identification without extensive model computations, as depicted in ROC curves.

Privacy-Utility Considerations

Despite the potential privacy leaks associated with embedding-driven anonymization, the paper finds that certain pseudo x-vector construction methods, such as Random Single, offer improved privacy while maintaining utility. However, the inadvertent leakage of non-x-vector features during anonymization merits further exploration.

Future Directions

This research challenges the sufficiency of model-based evaluations for privacy in x-vector anonymization. Future work should consider the intricacies of anonymization processes to balance privacy and utility efficiently. The prospect of incorporating user-centric utility assessments could also influence pseudo x-vector methodologies.

Overall, the paper calls for the reassessment of privacy-utility tradeoffs, encouraging sharper scrutiny of x-vector transformations to bolster speaker anonymization frameworks.