Speech recordings are being more frequently used to detect and monitor disease, leading to privacy concerns. Beyond cryptography, protection of speech can be addressed by approaches, such as perturbation, disentanglement, and re-synthesis, that eliminate sensitive information of the speaker, leaving the information necessary for medical analysis purposes. In order for such privacy protective approaches to be developed, clear and systematic specifications of assumptions concerning medical settings and the needs of medical professionals are necessary. In this paper, we propose a Scenario of Use Scheme that incorporates an Attacker Model, which characterizes the adversary against whom the speaker's privacy must be defended, and a Protector Model, which specifies the defense. We discuss the connection of the scheme with previous work on speech privacy. Finally, we present a concrete example of a specified Scenario of Use and a set of experiments about protecting speaker data against gender inference attacks while maintaining utility for Parkinson's detection.

翻译：语音记录正日益频繁地用于疾病检测与监测，由此引发隐私关切。除加密技术外，语音保护可通过扰动、解耦与重合成等方法实现，这些方法能消除说话者的敏感信息，同时保留医疗分析所需的有效信息。为开发此类隐私保护方法，必须对医疗场景假设及医疗专业人员需求进行清晰系统的规范。本文提出一种包含攻击者模型与保护者模型的使用场景方案：攻击者模型用于刻画需防范的隐私威胁方，保护者模型则明确防御机制。我们探讨了该方案与既往语音隐私研究的关联，最后通过具体案例展示规范化的使用场景，并设计实验验证在保持帕金森病检测效用的同时防御性别推断攻击的效果。