Preserving privacy in speaker and speech characterisation

Nautsch, Andreas; Jiménez, Abelino; Treiber, Amos; Kolberg, Jascha; Jasserand, Catherine; Kindt, Els; Delgado, Héctor; Todisco, Massimiliano; Hmani, Mohamed Amine; Mtibaa, Aymen; Abdelraheem, Mohammed Ahmed; Abad, Alberto; Teixeira, F. S.; Matrouf, Driss; Gomez‐Barrero, Marta; Petrovska‐Delacrétaz, Dijana; Chollet, Gérard; Evans, Nicholas; Schneider, Thomas; Bonastre, Jean-François; Raj, Bhiksha; Trancoso, Isabel; Busch, Christoph

doi:10.1016/j.csl.2019.06.001

Cited by 99 publications

(65 citation statements)

References 48 publications

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“…But since inferred information can be misused in countless ways [17,18], robust data protection mechanisms are needed in order to reap the benefits of voice and speech analysis in a socially acceptable manner. At the technical level, many approaches have been developed for privacy protection at different stages of the data life cycle, including operations over encrypted data, differential privacy, data anonymization, secure multiparty computation, and privacy-preserving data processing on edge devices [46,72,106]. Various privacy safeguards have been specifically designed or adjusted for audio mining applications.…”

Section: Discussionmentioning

confidence: 99%

“…These include voice binarization, hashing techniques for speech data, fully homomorphic inference systems, differential private learning, the computation of audio data in separate entrusted units, and speaker de-identification by voice transformation [72,73]. A comprehensive review of cryptography-based solutions for speech data is provided in [72]. Privacy risks can also be moderated by storing and processing only the audio data required for an application's functionality.…”

Section: Discussionmentioning

confidence: 99%

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Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Kröger

Lutz

Raschke

2020

IFIP Advances in Information and Communication Technology

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Internet-connected devices, such as smartphones, smartwatches, and laptops, have become ubiquitous in modern life, reaching ever deeper into our private spheres. Among the sensors most commonly found in such devices are microphones. While various privacy concerns related to microphone-equipped devices have been raised and thoroughly discussed, the threat of unexpected inferences from audio data remains largely overlooked. Drawing from literature of diverse disciplines, this paper presents an overview of sensitive pieces of information that can, with the help of advanced data analysis methods, be derived from human speech and other acoustic elements in recorded audio. In addition to the linguistic content of speech, a speaker's voice characteristics and manner of expression may implicitly contain a rich array of personal information, including cues to a speaker's biometric identity, personality, physical traits, geographical origin, emotions, level of intoxication and sleepiness, age, gender, and health condition. Even a person's socioeconomic status can be reflected in certain speech patterns. The findings compiled in this paper demonstrate that recent advances in voice and speech processing induce a new generation of privacy threats.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Kröger

Lutz

Raschke

2020

IFIP Advances in Information and Communication Technology

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“…Learning privacy preserving representations in speech data is relatively unexplored [46]. In [61] Nautsch et al investigate the importance of the development of privacy-preserving technologies to protect speech signals and highlight the importance of applying these technologies to protect speakers and speech characterization in recordings. Some recent works have sought to protect speaker identity [67], gender identity [33] and emotion [2].…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving Voice Analysis via Disentangled Representations

Aloufi

Haddadi

Boyle

2020

Proceedings of the 2020 ACM SIGSAC Conference on Cloud Computing Security Workshop

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Voice User Interfaces (VUIs) are increasingly popular and built into smartphones, home assistants, and Internet of Things (IoT) devices. Despite offering an always-on convenient user experience, VUIs raise new security and privacy concerns for their users. In this paper, we focus on attribute inference attacks in the speech domain, demonstrating the potential for an attacker to accurately infer a target user's sensitive and private attributes (e.g. their emotion, sex, or health status) from deep acoustic models. To defend against this class of attacks, we design, implement, and evaluate a user-configurable, privacy-aware framework for optimizing speechrelated data sharing mechanisms. Our objective is to enable primary tasks such as speech recognition and user identification, while removing sensitive attributes in the raw speech data before sharing it with a cloud service provider. We leverage disentangled representation learning to explicitly learn independent factors in the raw data. Based on a user's preferences, a supervision signal informs the filtering out of invariant factors while retaining the factors reflected in the selected preference. Our experimental evaluation over five datasets shows that the proposed framework can effectively defend against attribute inference attacks by reducing their success rates to approximately that of guessing at random, while maintaining accuracy in excess of 99% for the tasks of interest. We conclude that negotiable privacy settings enabled by disentangled representations can bring new opportunities for privacy-preserving applications.

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“…This method contains a language model during the hybrid ASR system's training, on which the userlevel DP can be applied to obscure the identity, at the sacrifice of transcription performance. The speaker and speech characterization process is protected in [23] by inserting noise during the learning process. However, due to strict black-box access and the lack of output probability information, our auditor's performance remains unknown on auditing the ASR model with DP.…”

Section: Threats To Auditors' Validitymentioning

confidence: 99%

The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

Miao

Xue

Chen

et al. 2020

Proceedings on Privacy Enhancing Technologies

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With the rapid development of deep learning techniques, the popularity of voice services implemented on various Internet of Things (IoT) devices is ever increasing. In this paper, we examine user-level membership inference in the problem space of voice services, by designing an audio auditor to verify whether a specific user had unwillingly contributed audio used to train an automatic speech recognition (ASR) model under strict black-box access. With user representation of the input audio data and their corresponding translated text, our trained auditor is effective in user-level audit. We also observe that the auditor trained on specific data can be generalized well regardless of the ASR model architecture. We validate the auditor on ASR models trained with LSTM, RNNs, and GRU algorithms on two state-of-the-art pipelines, the hybrid ASR system and the end-to-end ASR system. Finally, we conduct a real-world trial of our auditor on iPhone Siri, achieving an overall accuracy exceeding 80%. We hope the methodology developed in this paper and findings can inform privacy advocates to overhaul IoT privacy.

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Preserving privacy in speaker and speech characterisation

Cited by 99 publications

References 48 publications

Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Privacy Implications of Voice and Speech Analysis – Information Disclosure by Inference

Privacy-preserving Voice Analysis via Disentangled Representations

The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services

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