Towards End-to-End Private Automatic Speaker Recognition

The development of privacy-preserving automatic speaker verification systems has been the focus of a number of studies with the intent of allowing users to authenticate themselves without risking the privacy of their voice. However, current privacy-preserving methods assume that the template voice representations (or speaker embeddings) used for authentication are extracted locally by the user. This poses two important issues: first, knowledge of the speaker embedding extraction model may create security and robustness liabilities for the authentication system, as this knowledge might help attackers in crafting adversarial examples able to mislead the system; second, from the point of view of a service provider the speaker embedding extraction model is arguably one of the most valuable components in the system and, as such, disclosing it would be highly undesirable. In this work, we show how speaker embeddings can be extracted while keeping both the speaker's voice and the service provider's model private, using Secure Multiparty Computation. Further, we show that it is possible to obtain reasonable trade-offs between security and computational cost. This work is complementary to those showing how authentication may be performed privately, and thus can be considered as another step towards fully private automatic speaker recognition.Comment: Accepted for publication at Interspeech 202

Privacy-oriented manipulation of speaker representations

Speaker embeddings are ubiquitous, with applications ranging from speaker recognition and diarization to speech synthesis and voice anonymisation. The amount of information held by these embeddings lends them versatility, but also raises privacy concerns. Speaker embeddings have been shown to contain information on age, sex, health and more, which speakers may want to keep private, especially when this information is not required for the target task. In this work, we propose a method for removing and manipulating private attributes from speaker embeddings that leverages a Vector-Quantized Variational Autoencoder architecture, combined with an adversarial classifier and a novel mutual information loss. We validate our model on two attributes, sex and age, and perform experiments with ignorant and fully-informed attackers, and with in-domain and out-of-domain data

Privacy-Preserving iVector-Based Speaker Verification

This paper introduces an efficient algorithm to develop a privacy-preserving voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice print instead of alphanumeric passwords. Since a voice print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this paper proposes a novel technique based on randomization to carry out voice authentication, which allows the user to enrol and verify their voice in the randomized domain. To achieve this, the iVector-based voice verification technique has been redesigned to work on the randomized domain. The proposed algorithm is validated using a well-known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomization operations

Privacy-preserving Representation Learning for Speech Understanding

Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.Comment: INTERSPEECH 202

Analyzing and Applying Cryptographic Mechanisms to Protect Privacy in Applications

Privacy-Enhancing Technologies (PETs) emerged as a technology-based response to the increased collection and storage of data as well as the associated threats to individuals' privacy in modern applications. They rely on a variety of cryptographic mechanisms that allow to perform some computation without directly obtaining knowledge of plaintext information. However, many challenges have so far prevented effective real-world usage in many existing applications. For one, some mechanisms leak some information or have been proposed outside of security models established within the cryptographic community, leaving open how effective they are at protecting privacy in various applications. Additionally, a major challenge causing PETs to remain largely academic is their practicality-in both efficiency and usability. Cryptographic mechanisms introduce a lot of overhead, which is mostly prohibitive, and due to a lack of high-level tools are very hard to integrate for outsiders. In this thesis, we move towards making PETs more effective and practical in protecting privacy in numerous applications. We take a two-sided approach of first analyzing the effective security (cryptanalysis) of candidate mechanisms and then building constructions and tools (cryptographic engineering) for practical use in specified emerging applications in the domain of machine learning crucial to modern use cases. In the process, we incorporate an interdisciplinary perspective for analyzing mechanisms and by collaboratively building privacy-preserving architectures with requirements from the application domains' experts. Cryptanalysis. While mechanisms like Homomorphic Encryption (HE) or Secure Multi-Party Computation (SMPC) provably leak no additional information, Encrypted Search Algorithms (ESAs) and Randomization-only Two-Party Computation (RoTPC) possess additional properties that require cryptanalysis to determine effective privacy protection. ESAs allow for search on encrypted data, an important functionality in many applications. Most efficient ESAs possess some form of well-defined information leakage, which is cryptanalyzed via a breadth of so-called leakage attacks proposed in the literature. However, it is difficult to assess their practical effectiveness given that previous evaluations were closed-source, used restricted data, and made assumptions about (among others) the query distribution because real-world query data is very hard to find. For these reasons, we re-implement known leakage attacks in an open-source framework and perform a systematic empirical re-evaluation of them using a variety of new data sources that, for the first time, contain real-world query data. We obtain many more complete and novel results where attacks work much better or much worse than what was expected based on previous evaluations. RoTPC mechanisms require cryptanalysis as they do not rely on established techniques and security models, instead obfuscating messages using only randomizations. A prominent protocol is a privacy-preserving scalar product protocol by Lu et al. (IEEE TPDS'13). We show that this protocol is formally insecure and that this translates to practical insecurity by presenting attacks that even allow to test for certain inputs, making the case for more scrutiny of RoTPC protocols used as PETs. This part of the thesis is based on the following two publications: [KKM+22] S. KAMARA, A. KATI, T. MOATAZ, T. SCHNEIDER, A. TREIBER, M. YONLI. “SoK: Cryptanalysis of Encrypted Search with LEAKER - A framework for LEakage AttacK Evaluation on Real-world data”. In: 7th IEEE European Symposium on Security and Privacy (EuroS&P’22). Full version: https://ia.cr/2021/1035. Code: https://encrypto.de/code/LEAKER. IEEE, 2022, pp. 90–108. Appendix A. [ST20] T. SCHNEIDER , A. TREIBER. “A Comment on Privacy-Preserving Scalar Product Protocols as proposed in “SPOC””. In: IEEE Transactions on Parallel and Distributed Systems (TPDS) 31.3 (2020). Full version: https://arxiv.org/abs/1906.04862. Code: https://encrypto.de/code/SPOCattack, pp. 543–546. CORE Rank A*. Appendix B. Cryptographic Engineering. Given the above results about cryptanalysis, we investigate using the leakage-free and provably-secure cryptographic mechanisms of HE and SMPC to protect privacy in machine learning applications. As much of the cryptographic community has focused on PETs for neural network applications, we focus on two other important applications and models: Speaker recognition and sum product networks. We particularly show the efficiency of our solutions in possible real-world scenarios and provide tools usable for non-domain experts. In speaker recognition, a user's voice data is matched with reference data stored at the service provider. Using HE and SMPC, we build the first privacy-preserving speaker recognition system that includes the state-of-the-art technique of cohort score normalization using cohort pruning via SMPC. Then, we build a privacy-preserving speaker recognition system relying solely on SMPC, which we show outperforms previous solutions based on HE by a factor of up to 4000x. We show that both our solutions comply with specific standards for biometric information protection and, thus, are effective and practical PETs for speaker recognition. Sum Product Networks (SPNs) are noteworthy probabilistic graphical models that-like neural networks-also need efficient methods for privacy-preserving inference as a PET. We present CryptoSPN, which uses SMPC for privacy-preserving inference of SPNs that (due to a combination of machine learning and cryptographic techniques and contrary to most works on neural networks) even hides the network structure. Our implementation is integrated into the prominent SPN framework SPFlow and evaluates medium-sized SPNs within seconds. This part of the thesis is based on the following three publications: [NPT+19] A. NAUTSCH, J. PATINO, A. TREIBER, T. STAFYLAKIS, P. MIZERA, M. TODISCO, T. SCHNEIDER, N. EVANS. Privacy-Preserving Speaker Recognition with Cohort Score Normalisation”. In: 20th Conference of the International Speech Communication Association (INTERSPEECH’19). Online: https://arxiv.org/abs/1907.03454. International Speech Communication Association (ISCA), 2019, pp. 2868–2872. CORE Rank A. Appendix C. [TNK+19] A. TREIBER, A. NAUTSCH , J. KOLBERG , T. SCHNEIDER , C. BUSCH. “Privacy-Preserving PLDA Speaker Verification using Outsourced Secure Computation”. In: Speech Communication 114 (2019). Online: https://encrypto.de/papers/TNKSB19.pdf. Code: https://encrypto.de/code/PrivateASV, pp. 60–71. CORE Rank B. Appendix D. [TMW+20] A. TREIBER , A. MOLINA , C. WEINERT , T. SCHNEIDER , K. KERSTING. “CryptoSPN: Privacy-preserving Sum-Product Network Inference”. In: 24th European Conference on Artificial Intelligence (ECAI’20). Full version: https://arxiv.org/abs/2002.00801. Code: https://encrypto.de/code/CryptoSPN. IOS Press, 2020, pp. 1946–1953. CORE Rank A. Appendix E. Overall, this thesis contributes to a broader security analysis of cryptographic mechanisms and new systems and tools to effectively protect privacy in various sought-after applications

Privacy-preserving iVector-based speaker verification

This work introduces an efficient algorithm to develop a privacy-preserving (PP) voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice-print instead of alphanumeric passwords. Since a voice-print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this work proposes a novel technique based on randomisation to carry out voice authentication, which allows the user to enrol and verify their voice in the randomised domain. To achieve this, the iVector based voice verification technique has been redesigned to work on the randomised domain. The proposed algorithm is validated using a well known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomisation operations