Voice Privacy Preservation with Multiple Random Orthogonal Secret Keys: Attack Resistance Analysis

Abstract

Recently, opportunities to transmit speech data to deep learning models executed in the cloud have increased. This has led to growing concerns about speech privacy, including both speaker-specific information and the linguistic content of utterances. As an approach to preserving speech privacy, a speech privacy-preserving method based on encryption using a secret key with a random orthogonal matrix has been proposed. This method enables cloud-based model inference while concealing both the speech content and the speaker identity. However, the method has limited attack resistance and is constrained in terms of the deep learning models to which the encryption can be applied. In this work, we propose a method that enhances the attack resistance of the conventional speech privacy-preserving technique by employing multiple random orthogonal matrices as secret keys. We also introduce approaches to relax the model constraints, enabling the application of our method to a broader range of deep learning models. Furthermore, we investigate the robustness of the proposed method against attacks using extended attack scenarios based on the scenarios employed in the Voice Privacy Challenge. Our experimental results confirmed that the proposed method maintains privacy protection performance for speaker concealment, even under more powerful attack scenarios not considered in prior work.