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In many situations, such as in biometric applications, there is need to encrypt and “hide” data, while simultaneously permitting restricted computations on them. We present a method to securely determine the ℓ2 distance between two signals if they are close enough. This method relies on a locality sensitive hashing scheme based on a secure modular embedding, computed using quantized random projections,...
This paper investigates a new privacy-preserving paradigm for the task of Query-by-Example Speech Search using Secure Binary Embeddings, a hashing method that converts vector data to bit strings through a combination of random projections followed by banded quantization. The proposed method allows performing spoken query search in an encrypted domain, by analyzing ciphered information computed from...
Remote speaker verification services typically rely on the system having access to the users recordings, or features derived from them, and/or a model for the users voice. This conventional approach raises several privacy concerns. In this work, we address this privacy problem in the context of a speaker verification system using a factor analysis based front-end extractor, the so-called i-vectors...
In this paper we present a privacy-preserving speaker verification system using a UBM-GMM technique. Remote speaker verification services rely on the system having access to the user's recordings, or features derived from them, and a model representing the user's voice. Preserving privacy in our context means that neither the system observes voice samples or speech models from the user nor the user...
This paper addresses privacy concerns in voice biometrics. Conventional remote speaker verification systems rely on the system to have access to the user's recordings, or features derived from them, and also a model of the user's voice. In the proposed approach, the system has access to none of them. The supervectors extracted from the user's recordings are transformed to bit strings in a way that...
Secure multi-party computation based techniques are often used to perform audio database search tasks, such as music matching, with privacy. However, in spite of the security of individual components of the matching schemes, the overall scheme may still not be secure. This paper explains how such flaws may occur, using a privacy preserving music matching problem as a template, and provides a solution,...
This paper presents an implementation of a privacy-preserving music database matching algorithm, showing how privacy is achieved at the cost of computational complexity and execution time. The paper presents not only implementation details but also an analysis of the obtained results in terms of communication between the two parties, computational complexity, execution time and correctness of the...
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