Privacy-Preserving Machine Learning for Speech Processing (Springer Theses)
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Pathak presents solutions for privacy-preserving speech processing applications such as speaker verification, speaker identification and speech recognition. Ukaguzi Sera ya Maoni. Published on. Original pages. Best For. Web, Tablet. Content Protection.
Ripoti kuwa haifai. Itasawazishwa kiotomatiki kwenye akaunti yako na kukuruhusu usome vitabu mtandaoni au nje ya mtandao popote ulipo. Unaweza kusoma vitabu vilivyonunuliwa kwenye Google Play kwa kutumia kivinjari wavuti cha kompyuta yako. Tafadhali fuata maagizo ya kina katika Kituo cha usaidizi ili uweze kuhamishia faili kwenye Visomaji pepe vinavyotumika.
More related to speech recognition. Angalia zingine. Speech Dereverberation. Patrick A.
Speech dereverberation is a signal processing technique of key importance for successful hands-free speech acquisition in applications of telecommunications and automatic speech recognition. Over the last few years, speech dereverberation has become a hot research topic driven by consumer demand, the availability of terminals based on SkypeTM which encourage hands-free operation and the development of promising signal processing algorithms.
Speech Dereverberation gathers together an overview, a mathematical formulation of the problem and the state-of-the-art solutions for dereverberation. Information Security for Automatic Speaker Identification. Fathi E. Abd El-Samie. The author covers the fundamentals of both information and communication security including current developments in some of the most critical areas of automatic speech recognition.
In this paper, the aim is to enable multiple parties or servers to cooperatively build the ELM classification model without retrieving their own confidential input data set. Our main assumption is that the input data set is divided between two or more parties that are willing to train an ELM classifier if nothing beyond the expected end results is revealed 2.
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Our other assumption is that all parties follow the protocol; this is called a semi-honest security model 18 , In this work, two or more parties that hold vertically partitioned data are considered. The Paillier encryption scheme operates only on integer numbers. Thus, the proposed protocols manipulate only integers. However, the ELM classification algorithm is typically applied to continuous data.
Nonetheless, in the case of an input data set with real numbers in the protocol, we need to map floating point input data vectors into the discrete domain with a conversion function i. As denoted in Eq. Each element of matrix H is computed with activation function g. From Eq.
2. Related Works
We define the converting of the flow of messages into the flow of methods. In this section, a cloud-based privacy-preserving multi-party CPP-ELM learning algorithm over arbitrarily partitioned data is presented. Let x min and x max be an input data set of minimum and maximum values of each corresponding feature. Algorithm 1 shows the overall process in the initialization phase. Algorithm 2 shows the overall process. The party server knows the plaintext weight vectors w , b.
Algorithm 3 shows the calculation of the hidden layer output matrix for each server in the encrypted domain. The client can now compute the last step given in Fig. Algorithm 4 shows the classifier model building in the decrypted domain. We have implemented our proposed protocols and the classifier training phase in Python by using the scikit-learn library for machine learning and the PyPhe library for the partially homomorphic encryption implementation. Table 5. In plain-domain training, the conventional ELM training phase is performed for all data sets in a single pass.
All experiments are repeated 5 times and the results are averaged. The Ionosphere data set consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6. The data set contains training instances. The Sonar data set is used for the study of the classification of sonar signals using a neural network.
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The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock 7. The Breast Cancer data set contains instances, with benign Each instance is described by 9 attributes with an integer value The Australian data set concerns credit card applications.
The data set contains instances and each instance is described by 14 attributes Table 2 shows the best performance of the conventional ELM method of each experimental data set. Both the server and the clients are modeled as different process with the Python multiprocessing library.
Each process sends the variables to each other via file exchange. The developed software is tested on a computer with 1. The system has been tested while varying the party size of each data set from 3 to 10, and with key lengths of and bits. Performance results are shown in Table 3. It is noted that the average training and model building time depends on the total number of instances used for classification. This process is computed at the client-side and can be pre-computed offline. In the proposed method, CPP-ELM, there are three different factors party size, complexity of the input data set, and key bit length that affect the computation time according to the experimental results.
As show in Fig. In cryptographic systems, all computations work with integers. Thus, all real numbers used in our algorithms are mapped into finite fields by using a scaling procedure. Before starting the learning phase, the client converts the input data set to integer representation by multiplying and rounding the results. In the last stage, in Algorithm 4 , the client scales down the hidden layer output matrix H.
This scaling operation causes accuracy loss in the final classifier model. In this work, we proposed a privacy-preserving and practical multi-party ELM learning scheme over arbitrarily vertically partitioned data between two or more parties. We have also provided cryptographically secure protocols for computing the hidden layer output matrix and have shown how to aggregate encrypted intermediate matrices securely.
In our proposed approach, the client encrypts its input data, creates arbitrarily and vertically partitioned data, and then uploads the encrypted messages to a cloud system. The cloud system can execute the most time-consuming operation of ELM training without knowing any confidential information. One interesting future work is to extend the privacy-preserving training to other existing classification algorithms. We suggest that the method proposed here is appropriate for areas such as healthcare where data privacy is highly sensitive.
Data privacy is protected by transferring the identification information to the remote cloud service provider in an encrypted manner.
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Using the high computational power provided by the cloud service provider, the classification model is built on the encrypted data set. In this way, the cloud service provider can create a classification model without reaching plaintext data.