Speech being a unique characteristic of an individual is widely used in speaker verification and speaker identification tasks in applications such as authentication and surveillance respectively. In this article, we present frameworks for privacy-preserving speaker verification and speaker identification systems, where the system is able to perform the necessary operations without being able to observe the speech input provided by the user. In a speech-based authentication setting, this privacy constraint protect against an adversary who can break into the system and use the speech models to impersonate legitimate users. In surveillance applications, we require the system to first identify if the speech recording belongs to a suspect while preserving the privacy constraints. This prevents the system from listening in on conversations of innocent individuals. In this paper we formalize the privacy criteria for the speaker verification and speaker identification problems and construct Gaussian mixture model-based protocols. We also report experiments with a prototype implementation of the protocols on a standardized dataset for execution time and accuracy.

We present a text-independent privacy-preserving speaker verification system that functions similar to conventional password-based authentication. Our privacy constraints require that the system does not observe the speech input provided by the user, as this can be used by an adversary to impersonate the user in the same system or elsewhere. We represent the speech input using supervectors and apply locality sensitive hashing (LSH) to transform these into bit strings, where two supervectors, and therefore inputs, are likely to be similar if they map to the same string. This transformation, therefore, reduces the problem of identifying nearest neighbors to string comparison. The users then apply a cryptographic hash function to the strings obtained from their enrollment and verification data, thereby obfuscating it from the server, who can only check if two hashed strings match without being able to reconstruct their content. We present execution time and accuracy experiments with the system on the YOHO dataset, and observe that the system achieves acceptable accuracy with minimal computational overhead needed to satisfy the privacy constraints.

As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealing information about individual data instances. The differential privacy model provides a framework for the development and theoretical analysis of such mechanisms. In this paper, we propose an algorithm for learning a discriminatively trained multiclass Gaussian mixture model-based classifier that preserves differential privacy using a large margin loss function with a perturbed regularization term. We present a theoretical upper bound on the excess risk of the classifier introduced by the perturbation.

Alice possesses a sample of private data from which she wishes to obtain some probabilistic inference. Bob possesses Hidden Markov Models (HMMs) for this purpose, but he wants the model parameters to remain private. This paper develops a framework that enables Alice and Bob to collaboratively compute the so-called forward algorithm for HMMs while satisfying their privacy constraints. This is achieved using a public-key additively homomorphic cryptosystem. Our framework is asymmetric in the sense that a larger computational overhead is incurred by Bob who has higher computational resources at his disposal, compared with Alice who has limited computing resources. Practical issues such as the encryption of probabilities and the effect of finite precision on the accuracy of probabilistic inference are considered. The protocol is implemented in software and used for secure keyword recognition.

In this paper we present an adapted UBM-GMM based privacy preserving speaker verification (PPSV) system, where the system is not able to observe the speech data provided by the user and the user does not observe the models trained by the system. These privacy criteria are important in order to prevent an adversary having unauthorized access to the user’s client device from impersonating a user and also from another adversary who can break into the verification system can learn about the user’s speech patterns to impersonate the user in another system. We present protocols for speaker enrollment and verification which preserve privacy according to these requirements and report experiments with a prototype implementation on the YOHO dataset. Index Terms: speaker verification, secure biometrics 1.

As increasing amounts of sensitive personal information finds its way into data repositories, it is important to develop analysis mechanisms that can derive aggregate information from these repositories without revealing information about individual data instances. Though the differential privacy model provides a framework to analyze such mechanisms for databases belonging to a single party, this framework has not yet been considered in a multi-party setting. In this paper, we propose a privacy-preserving protocol for composing a differentially private aggregate classifier using classifiers trained locally by separate mutually untrusting parties. The protocol allows these parties to interact with an untrusted curator to construct additive shares of a perturbed aggregate classifier. We also present a detailed theoretical analysis containing a proof of differential privacy of the perturbed aggregate classifier and a bound on the excess risk introduced by the perturbation. We verify the bound with an experimental evaluation on a real dataset.