Privacy PORCUPINE: Anonymization of Speaker Attributes Using Occurrence Normalization for Space-Filling Vector Quantization

Speech signals contain a vast range of private information such as its text, speaker identity, emotions, and state of health. Privacy-preserving speech processing seeks to filter out any private information that is not needed for downstream tasks, for example with an information bottleneck, sufficiently tight that only the desired information can pass through. We however demonstrate that the occurrence frequency of codebook elements in bottlenecks using vector quantization have an uneven information rate, threatening privacy. We thus propose to use space-filling vector quantization (SFVQ) together with occurrence normalization, balancing the information rate and thus protecting privacy. Our experiments with speaker identification validate the proposed method. This approach thus provides a generic tool for quantizing information bottlenecks in any speech applications such that their privacy disclosure is predictable and quantifiable.